[Agresti, 1990] Agresti, A. (1990). Categorical Data Analysis. Wiley.Google Scholar

[Aine et al, 1998] Aine, C.J., Huang, M., Christner, R., Stephen, J., Meyer, J., Silveri, J., & Weisend, M. (1998). New developments in source localization algorithms: clinical examples. International Journal of Psychophysiology, 30, 198.CrossRefGoogle Scholar

[Akaike, 1973] Akaike, H. (1973). Information theory and the extension of the maximum likelihood principle. Pages 267-281 of: Petrov, B.N., & Csaki, F. (eds), Second International Symposium on Information Theory. Akademiai Kiado.Google Scholar

[Amari, 1985] Amari, S.-I. (1985). Differential Geometrical Methods in Statistics. Lecture Notes in Statistics, vol. 28. Springer.CrossRefGoogle Scholar

[Amari, 1997] Amari, S.-I. (1997). Neural learning in structured parameter spaces – natural Riemannian gradient. Pages 127-133 of: Advances in Neural Information Processing Systems, vol. 9. MIT Press.Google Scholar

[Amari, 1998] Amari, S.-I. (1998). Natural gradient works efficiently in learning. Neural Computation, 10(2), 251–276.CrossRefGoogle Scholar

[Amari et al, 1996] Amari, S.-I., Cichocki, A., & Yang, H. (1996). A new learning algorithm for blind signal separation. Pages 757-763 of: Touretzky, D., Mozer, M., & Hasselmo, M. (eds), Advances in Neural Information Processing Systems, vol. 8.Google Scholar

[Amari et al., 1997] Amari, S.-I., Chen, T.-P., & Cichocki, A. (1997). Stability analysis of adaptive blind source separation. Neural Networks, 10(8), 1345–1351.CrossRefGoogle Scholar

[Amari et al, 1998] Amari, S.-I., Douglas, S.C., & Cichocki, A. (1998). Multichannel blind deconvolution and source separation using the natural gradient. IEEE Transactions on Signal Processing, 101-104.Google Scholar

[Atick & Redlich, 1990] Atick, J.J., & Redlich, A.N. (1990). Towards a theory of early visual processing. Neural Computation, 2, 308–320.CrossRefGoogle Scholar

[Atick et al, 1995] Atick, J.J., Griffin, P.A., & Redlich, A.N. (1995). Statistical approach to shape from shading: reconstruction of 3-dimensional face surfaces from single 2-dimensional images. Neural Computation, 8(6), 1321–1340.Google Scholar

[Attias, 1999a] Attias, H. (1999a). Independent factor analysis. Neural Computation, 11(5), 803–852.CrossRefGoogle ScholarPubMed

[Attias, 1999b] Attias, H. (1999b). Inferring parameters and structure of latent variable models by variational Bayes. Pages 21-30 of: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence.Google Scholar

[Attias, 2000] Attias, H. (2000). A variational bayesian framework for graphical models. T., Leenet al. (eds), Advances in Neural Information Processing Systems, vol. 12. MIT Press. Available from http://www.gatsby.ucl.ac.uk/~hagai/papers.html.Google Scholar

[Attias & Schreiner, 1998] Attias, H., & Schreiner, C.E. (1998). Blind source separation and deconvolution – the dynamic component analysis algorithm. Neural Computation, 10(6), 1373–1424.CrossRefGoogle ScholarPubMed

[Back & Weigend, 1998] Back, A.D., & Weigend, A.S. (1998). A first application of independent component analysis to extracting structure from stock returns. International Journal on Neural Systems, 8(4), 473–484.Google Scholar

[Bae et al., 2000] Bae, U.-M., Lee, T.-W., & Lee, S.-Y. (2000). Blind signal separation in teleconferencing using the ICA mixture model. IEE Electronics Letters, 36(7), 680–682.CrossRefGoogle Scholar

[Barber & Bishop, 1998] Barber, D., & Bishop, C.M. (1998). Ensemble learning in Bayesian neural networks. Pages 215–237 of: Jordan, M.I., Kearns, M.J., & Solla, S.A. (eds), Advances in Neural Information Processing Systems, vol. 10. MIT Press.Google Scholar

[Barlow, 1961a] Barlow, H.B. (1961a). The coding of sensory messages. Pages 330-360 of: Thorpe, & Zangwill (eds), Current Problems in Animal Behavior. Cambridge University Press.Google Scholar

[Barlow, 1961b] Barlow, H.B. (1961b). Possible principles underlying the transformation of sensory messages. Pages 217-234 of: Rosenblith, W.A. (ed), Sensory Communication. MIT Press.Google Scholar

[Barlow, 1989] Barlow, H.B. (1989). Unsupervised learning. Neural Computation, 1, 295–311.CrossRefGoogle Scholar

[Bar-Ness et al, 1982] Bar-Ness, Y., Carlin, J., & Steinberger, M. (1982). Bootstrapping adaptive cross-pol canceller for satellite communications. Pages 4F.5.1–4F.5.5 of: Proc IEEE Int Conf Comunications.Google Scholar

[Barros & Ohnishi, 1999] Barros, A.K., & Ohnishi, N. (1999). Removal of quasi-periodic sources from physiological measurements. Pages 185-190 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Bartlett et al, 1997] Bartlett, M.S., Stewart, M., & Sejnowski, T.J. (1997). Viewpoint invariant face recognition using independent component attractor networks. Pages 817-823 of: M., Mozer, M., Jordan, T., Petsche (ed), Advances in Neural Information Processing Systems, vol. 9. Available from http: //www.cnl.salk.edu/~marni.Google Scholar

[Bartlett et al, 1998] Bartlett, M.S., Lades, H.M., & Sejnowski, T.J. (1998). Independent components representations for face recognition. Pages 528-539 of: Rogowitz, & Pappas (eds), Proceedings of the SPIE Symposium on Electronic Imaging: Science and Technology: Conference on Human Vision and Electronic Imaging HI, vol. 3299. SPIE Press. Available from http://www.cnl.salk.edu/~mami.Google Scholar

[Beale & Mallows, 1959] Beale, E.M.L., & Mallows, C.L. (1959). Scale mixing of symmetric distributions with zero means. Annals of Mathematical Statistics, 30, 1145-1151.CrossRefGoogle Scholar

[Bell & Sejnowski, 1995] Bell, A.J., & Sejnowski, T.J. (1995). An information maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.CrossRefGoogle ScholarPubMed

[Bell & Sejnowski, 1997] Bell, A.J., & Sejnowski, T.J. (1997). The “independent components” of natural scenes are edge filters. Vision Research, 37(23), 3327–3338.CrossRefGoogle ScholarPubMed

[Belouchrani & Cardoso, 1994] Belouchrani, A., & Cardoso, J.-F. (1994). Maximum likelihood source separation for discrete sources. Pages 768-771 of: Proc EUSIPCO.Google Scholar

[Belouchrani & Cardoso, 1995] Belouchrani, A., & Cardoso, J.-F. (1995). Maximum likelihood source separation by the expectation-maximization technique: deterministic and stochastic implementation. Pages 49-53 of: Proceedings of 1995 International Symposium on Non-Linear Theory and Applications.Google Scholar

[Belouchrani et al., 1993] Belouchrani, A., Abed Meraim, K., Cardoso, J.-F., & Moulines, E. (1993). Second order blind separation of correlated sources. Pages 346-351 of: Proceedings of International Conference on Digital Signal Processing.Google Scholar

[Belouchrani et al., 1997] Belouchrani, A., Abed Meraim, K., Cardoso, J.-F., & Moulines, E. (1997). A blind source separation technique based on second order statistics. IEEE Transactions on Signal Processing, 45(2), 434–44.CrossRefGoogle Scholar

[Ben-Tal & Zibulevsky, 1997] Ben-Tal, A., & Zibulevsky, M. (1997). Penalty/barrier multiplier methods for convex programming problems. SIAM Journal on Optimization, 7(2), 347–366.CrossRefGoogle Scholar

[Bezdek, 1981] Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press.CrossRefGoogle Scholar

[Bingham & Hyvärinen, 2000] Bingham, E., & Hyvärinen, A. (2000). A fast fixed-point algorithm for independent component analysis of complex-valued signals. International Journal of Neural Systems, 10(1), 1–8.CrossRefGoogle ScholarPubMed

[Bishop, 1994] Bishop, C.M. (1994). Mixture density networks. Tech. rept. Neural Computing Research Group, Aston University.Google Scholar

[Bishop, 1995] Bishop, C.M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.Google Scholar

[Bishop, 1998] Bishop, C.M. (1998). GTM: the generative topographic mapping. Neural Computation, 10, 215–234.CrossRefGoogle Scholar

[Bishop, 1999] Bishop, C.M. (1999). Variational principal components. Pages 509-514 of: International Conference on Artificial Neural Networks.Google Scholar

[Bofill & Zibulevsky, 2000a] Bofill, P., & Zibulevsky, M. (2000a). Blind separation of more sources than mixtures using the sparsity of the short-time Fourier transform. Pajunen, P. (ed), International Workshop on Independent Component Analysis and Blind Signal Separation.Google Scholar

[Bofill & Zibulevsky, 2000b] Bofill, P., & Zibulevsky, M. (2000b). Sparse underdetermined ICA: estimating the mixing matrix and the sources separately. Tech. rept. UPC-DAC-2000-7. Universitat Politecnica de Catalunya. Available from http://www.ac.upe.es/homes/pau/sounds.html.Google Scholar

[Box & Tiao, 1973] Box, G., & Tiao, G. (1973). Bayesian Inference in Statistical Analysis. Wiley.Google Scholar

[Bradwood, 1978] Bradwood, D. (1978). Cross-coupled cancellation systems for improving cross-polarisation discrimination. Pages 41-45 of: Proceedings of IEEE International Conference on Antennas and Propagation, vol. I.Google Scholar

[Brandwood, 1983] Brandwood, D. (1983). A complex gradient operator and its application in adaptive array theory. I EE Proceedings, 130(1), 11–16.Google Scholar

[Bregman, 1990] Bregman, A.S. (1990). Auditory Scene Analysis. MIT Press.Google Scholar

[Brehm & Stammler, 1987] Brehm, H., & Stammler, W. (1987). Description and generation of spherically invariant speech-model signals. Signal Processing, 12, 119–141.CrossRefGoogle Scholar

[Buckheit et al, 1995] Buckheit, J., Chen, S.S., Donoho, D.L., Johnstone, I., & Scargle, J. (1995). About WaveLab. Tech. rept. Department of Statistics, Stanford University. Available from http://www-stat.Stanford.edu/~donoho/Reports/.Google Scholar

[Burel, 1992] Burel, G. (1992). Blind separation of sources: A nonlinear neural algorithm. Neural Networks, 5, 937–947.CrossRefGoogle Scholar

[Cardoso, 1994] Cardoso, J.-F. (1994). On the performance of orthogonal source separation algorithms. Pages 776-779 of: Proceedings of EUSIP CO.Google Scholar

[Cardoso, 1997] Cardoso, J.-F. (1997). Infomax and maximum likelihood for blind separation. IEEE Signal Processing Letters, 4(4), 112–114.CrossRefGoogle Scholar

[Cardoso, 1998a] Cardoso, J.-F. (1998a). Blind signal separation: statistical principles. Proceedings of the IEEE. Special issue on blind identification and estimation, 9(10), 2009-2025.Google Scholar

[Cardoso, 1998b] Cardoso, J.-F. (1998b). On the stability of source separation algorithms. Pages 13-22 of: Costantinides, A., Kung, S.-Y., Niranjan, M., & Wilson, E. (eds), Neural Networks for Signal Processing VIII. IEEE.Google Scholar

[Cardoso, 1999a] Cardoso, J.-F. (1999a). High-order contrasts for independent component analysis. Neural Computation, 11, 157–192.CrossRefGoogle ScholarPubMed

[Cardoso, 1999b] Cardoso, J.-F. (1999b). JADE for real-valued data. Available from http://sig.enst.fr:80~cardoso/guidesepsou.html.Google Scholar

[Cardoso, 2000] Cardoso, J.-F. (2000). On the stability of source separation algorithms. Journal of VLSI Signal Processing Systems, 26(1), 7–14. Available from http://tsi.enst.fr/~cardoso/.Google Scholar

[Cardoso & Comon, 1996] Cardoso, J.-F., & Comon, P. (1996). Independent component analysis, a survey of some algebraic methods. Pages 93-96 of: Proceedings of ISCAS'96, vol. 2. Available from ftp://sig.enst.fr/pub/jfc/Papers/iscas96_algebra.ps.gz.Google Scholar

[Cardoso & Laheld, 1996] Cardoso, J.-F., & Laheld, B. (1996). Equivariant adaptive source separation. IEEE Transactions on Signal Processing, 45(2), 434–444.Google Scholar

[Cardoso & Souloumiac, 1993] Cardoso, J.-F., & Souloumiac, A. (1993). Blind beamforming for non-Gaussian signals. IEE Proceedings F, 140(6), 362–370.Google Scholar

[Cardoso & Souloumiac, 1996] Cardoso, J.-F., & Souloumiac, A. (1996). Jacobi angles for simultaneous diagonalization. SI AM Journal of Matrix Analysis and Applications, 17(1), 161–164.Google Scholar

[Chen et al, 1995] Chen, S.S., Donoho, D.L., Saunders, M.A., Johnstone, I., & Scargle, J. (1995). About Atomizer. Tech. rept. Department of Statistics, Stanford University. Available from http://www-stat.Stanford.edu/~donoho/Reports/.Google Scholar

[Chen et al, 1996] Chen, S.S., Donoho, D.L., & Saunders, M.A. (1996). Atomic decomposition by basis pursuit. Tech. rept. Department of Statistics, Stanford University. Available from http://www-stat.Stanford.edu/~donoho/Reports/.Google Scholar

[Chevalier et al, 1999] Chevalier, P., Capdevielle, V., & Comon, P. (1999). Performance of HO blind source separation methods: experimental results on ionospheric HF links. Pages 443-448 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Chin et al, 1999] Chin, E., Weigend, A.S., & Zimmermann, H. (1999). Computing portfolio risk using Gaussian mixtures and independent component analysis. Proceedings of the 1999 IEEE/IAFE/INFORMS Conference on Computational Intelligence for Financial Engineering (CIFEr'99). IAFE.Google Scholar

[Choi et al., 1998] Choi, S., Cichocki, A., & Amari, S.-I. (1998). Flexible independent component analysis. Pages 83-92 of: Costantinides, A., Kung, S.-Y., Niranjan, M., & Wilson, E. (eds), Neural Networks for Signal Processing VIII. IEEE.Google Scholar

[Choudrey et al., 2001] Choudrey, R., Penny, W., & Roberts, S. (2001). An ensemble learning approach to independent component analysis. Neural Networks for Signal Processing X. IEEE. Available from http://www.robots.ox.ac.uk/~sjrob/pubs.html.Google Scholar

[Cichocki & Unbehauen, 1996] Cichocki, A., & Unbehauen, R. (1996). Robust neural networks with on-line learning for blind identification and blind separation of sources. IEEE Transactions on Circuits and Systems, 43(11), 894-906.Google Scholar

[Cichocki et al, 1999] Cichocki, A., Zhang, L., Choi, S., & Amari, S.-I. (1999). Nonlinear dynamic independent component analysis using state-space and neural network models. Pages 99-104 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Coifman & Wickerhauser, 1992] Coifman, R.R., & Wickerhauser, M.V. (1992). Entropy-based algorithms for best-basis selection. IEEE Transactions on Information Theory, 38, 713–718.CrossRefGoogle Scholar

[Coifman et al, 1992] Coifman, R.R., Meyer, Y., & Wickerhauser, M.V. (1992). Wavelet analysis and signal processing. Pages 153-178 of: Ruskai, M.B. (ed), Wavelets and their applications. Jones and Barlett.Google Scholar

[Comon, 1994] Comon, P. (1994). Independent component analysis, a new concept?Signal Processing, 36, 287–314.CrossRefGoogle Scholar

[Comon et al., 1991] Comon, P., Jutten, C., & Herault, J. (1991). Blind separation of sources. Part II: problems statement. Signal Processing, 24(1), 11–20.CrossRefGoogle Scholar

[Cook et al, 1993] Cook, D., Buja, A., & Cabrera, J. (1993). Projection pursuit indexes based on orthonormal function expansions. Journal of Computational and Graphical Statistics, 2(3), 225–250.CrossRefGoogle Scholar

[Cover & Thomas, 1991] Cover, T., & Thomas, J. (1991). Elements of Information Theory. Vol. 1. Wiley.CrossRefGoogle Scholar

[De Bonet & Viola, 1998] De Bonet, J.S., & Viola, P. (1998). A non-parametric multi-scale statistical model for natural images. Jordan, M.I., Kearns, M.J., & Solla, S.A. (eds), Advances in Neural Information Processing Systems, vol. 10. MIT Press.Google Scholar

[deCharms & Merzenich, 1998] deCharms, C.R., & Merzenich, M.M. (1998). Characteristic neurons in the primary auditory cortex of the awake primate using reverse correlation. Pages 124-130 of: Jordan, M.I., Kearns, M.J., & Solla, S.A. (eds), Advances in Neural Information Processing Systems, vol. 10.Google Scholar

[Deco & Brauer, 1995] Deco, G., & Brauer, W. (1995). Higher order statistical decorrelation by volume conserving neural architectures. Neural Networks, 8, 525–535.CrossRefGoogle Scholar

[Deco & Obradovic, 1996] Deco, G., & Obradovic, D. (1996). An Information Theoretic Approach to Neural Computing. Perspectives in Neural Computing. Springer.CrossRefGoogle Scholar

[Delfosse & Loubaton, 1995] Delfosse, N., & Loubaton, P. (1995). Adaptive blind separation of independent sources: a deflation approach. Signal Processing, 45, 59–83.CrossRefGoogle Scholar

[Dempster et al, 1976] Dempster, A.P., Laird, N.M., & Rubin, D.B. (1976). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1–38.Google Scholar

[Deville et al, 1999] Deville, Y., Damour, J., & Charkani, N. (1999). Improved multi-tag radio-frequency identification systems based on new source separation neural networks. Pages 449-454 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Donoho, 1981] Donoho, D. (1981). On minimum entropy deconvolution. Pages 565-608 of: Findley, D.F. (ed), Applied Time Series Analysis II. Academic Press.Google Scholar

[Douglas & Kung, 2000] Douglas, S.C., & Kung, S.Y. (2000). Gradient adaptive algorithms for contrast-based blind deconvolution. VLSI Signal Processing Journal, 26(1), 47–61.CrossRefGoogle Scholar

[Duda & Hart, 1973] Duda, R.O., & Hart, R.E. (1973). Pattern Classification and Scene Analysis. Wiley.Google Scholar

[Everitt, 1984] Everitt, B.S. (1984). An Introduction to Latent Variable Models. Chapman and Hall.CrossRefGoogle Scholar

[Everson & Roberts, 1999a] Everson, R.M., & Roberts, S.J. (1999a). ICA: a flexible non-linearity and decorrelating manifold approach. Neural Computation, 11(8), 1957–1983.CrossRefGoogle Scholar

[Everson & Roberts, 1999b] Everson, R.M., & Roberts, S.J. (1999b). Non-stationary independent components analysis. Proceedings of International Conference on Artificial Neural Networks (ICANN'99). IEE.Google Scholar

[Everson & Roberts, 2000a] Everson, R.M., & Roberts, S.J. (2000a). Independent component analysis. Pages 153–168 of: Lisboa, P.J.G., Ifeachor, E.C., & Szczepaniak, P.S. (eds), Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing. Springer.Google Scholar

[Everson & Roberts, 2000b] Everson, R.M., & Roberts, S.J. (2000b). Inferring the eigenvalues of covariance matrices from limited, noisy data. IEEE Transactions on Signal Processing, 48(7), 2083–2091.CrossRefGoogle Scholar

[Everson & Roberts, 2000c] Everson, R.M., & Roberts, S.J. (2000c). Measuring mutual information. Tech. rept. Exeter University. Available from http://www.dcs.ex.ac.uk/academics/reverson.Google Scholar

[Fearnhead, 1999] Fearnhead, P. (1999). Sequential Monte Carlo methods in filter theory. Ph.D. thesis, University of Oxford.Google Scholar

[Fety & Van Ulffelen, 1988] Fety, L., & Van Ulffelen, J.P. (1988). New methods for signal separation. Pages 226-230 of: Proceedings of 4th International Conference of HF radio systems and techniques. IEE.Google Scholar

[Flury & Gautschi, 1986] Flury, B.N., & Gautschi, W. (1986). An algorithm for the simultaneous orthogonal transformation of several positive definite symmetric matrices to nearly orthogonal form. SI AM Journal of Scientific and Statistical Computing, 7(1), 169-184.Google Scholar

[Fraser & Swinney, 1986] Fraser, A.M., & Swinney, H.L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review, 33A(2).Google Scholar

[Friedman, 1987] Friedman, J. (1987). Exploratory projection pursuit. Journal of the American Statistical Association, 82(397), 249–266.CrossRefGoogle Scholar

[Friedman & Tukey, 1974] Friedman, J., & Tukey, J. (1974). A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Neural Networks, 23, 881–889.Google Scholar

[Gaeta & Lacoume, 1990] Gaeta, M., & Lacoume, J.-L. (1990). Source separation without prior knowledge: the maximum likelihood solution. Pages 621-624 of: Proceedings of EUSIPO.Google Scholar

[Ghahramani & Beal, 2000] Ghahramani, Z., & Beal, M.J. (2000). Variational inference for Bayesian mixtures of factor analysers. Advances in Neural Information Processing Systems, vol. 12. MIT Press.Google Scholar

[Ghahramani & Hinton, 1997] Ghahramani, Z., & Hinton, G.E. (1997). The EM algorithm for mixtures of factor analyzers. Tech. rept. Department of Computer Science, University of Toronto.Google Scholar

[Ghahramani & Jordan, 1997] Ghahramani, Z., & Jordan, M.I. (1997). Factorial hidden Markov models. Machine Learning, 29, 245–273.CrossRefGoogle Scholar

[Ghahramani & Roweis, 1999] Ghahramani, Z., & Roweis, S. (1999). Learning nonlinear dynamical systems using an EM algorithm. Pages 599–605 of: Kearns, M.S., Solla, S.A., & Cohn, D.A. (eds), Advances in Neural Information Processing Systems, vol. 11. MIT Press.Google Scholar

[Gill et al., 1995] Gill, P.E., Murray, W., & Wright, M.H. (1995). Practical Optimization. 10th edn. Academic Press.Google Scholar

[Girolami, 1997] Girolami, M. (1997). Self-organising artificial neural networks for signal separation. Ph.D. thesis, Department of Computing and Information Systems, Paisley University.Google Scholar

[Girolami, 1998] Girolami, M. (1998). An alternative perspective on adaptive independent component analysis algorithms. Neural Computation, 10(8), 2103–2114.CrossRefGoogle ScholarPubMed

[Girolami, 1999a] Girolami, M. (1999a). Hierarchic dichotomizing of polychotomous data – an ICA based data mining tool. Pages 197-202 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Girolami, 1999b] Girolami, M. (1999b). Self-Organising Neural Networks -Independent Component Analysis and Blind Source Separation. Springer.Google Scholar

[Girolami, 2000a] Girolami, M. (2000a). Document representations based on generative multivariate Bernoulli latent topic models. Pages 194-201 of: Proceedings of the 22nd Annual Colloquium on Information Retrieval Research.Google Scholar

[Girolami, 2000b] Girolami, M. (2000b). Kernel based clustering and visualisation in feature space. Tech. rept. ISSN 1461-6122. Computing and Information Systems, University of Paisley.Google Scholar

[Girolami & Fyfe, 1997a] Girolami, M., & Fyfe, C. (1997a). Extraction of independent signal sources using deflationary exploratory projection pursuit network with lateral inhibition. IEE Proceedings on Vision. Image and Signal Processing, 14(5), 299–306.Google Scholar

[Girolami & Fyfe, 1997b] Girolami, M., & Fyfe, C. (1997b). Generalised independent component analysis through unsupervised learning with emergent Bussgang properties. Pages 1788-1891 of: Proceedings of International Conference on Neural Networks.Google Scholar

[Girolami et al, 1998] Girolami, M., Cichocki, A., & Amari, S.-I. (1998). A common neural network model for exploratory data analysis and independent component analysis. IEEE Transactions on Neural Networks, 9(6), 1495–1501.CrossRefGoogle ScholarPubMed

[Goldman, 1976] Goldman, J. (1976). Detection in the presence of spherically symmetric random vectors. IEEE Transactions on Information Theory, 22(1), 52–59.CrossRefGoogle Scholar

[Gordon et al, 1993] Gordon, N., Salmond, D., & Smith, A.F.M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140, 107–113.Google Scholar

[Gull & Daniell, 1978] Gull, S.F., & Daniell, G.J. (1978). Image reconstruction from incomplete and noisy data. Nature, 272(5655), 686–690.CrossRefGoogle Scholar

[Hansen, 2000] Hansen, L.K. (2000). Blind separation of noisy image mixtures. Pages 161–182 of: Girolami, M. (ed), Advances in Independent Component Analysis. Perspectives in Neural Computing. Springer.Google Scholar

[Haykin, 1994] Haykin, S. (1994). Blind Deconvolution. Information and System Sciences. Prentice-Hall.Google Scholar

[Haykin, 1998] Haykin, S. (1998). Neural Networks – a Comprehensive Foundation. 2nd edn. Prentice-Hall.Google Scholar

[Hecht-Nielsen, 1995] Hecht-Nielsen, R. (1995). Replicator neural networks for universal optimal source coding. Science, 269, 1860–1863.CrossRefGoogle ScholarPubMed

[Hecht-Nielsen, 1996] Hecht-Nielsen, R. (1996). Data manifolds, natural coordinates, replicator neural networks, and optimal source coding. Pages 41-45 of: Proceedings of 1996 International Conference on Neural Information Processing (ICONIP'96).Google Scholar

[Herault & Jutten, 1986] Herault, J., & Jutten, C. (1986). Space or time processing by neural network models. Denker, J.S. (ed), Proceedings AIP Conference: Neural Networks for Computing, vol. 151. American Institute for Physics.Google Scholar

[Hinton & Sejnowski, 1998] Hinton, G., & Sejnowski, T.J. (eds). (1998). Unsupervised Learning: Foundations of Neural Computation. MIT Press.Google Scholar

[Hinton & van Camp, 1993] Hinton, G.E., & van Camp, D. (1993). Keeping neural networks simple by minimizing the description length of the weights. Pages 5-13 of: Proceedings of the Sixth Annual Conference on Computational Learning Theory.Google Scholar

[Hochreiter & Schmidhuber, 1999] Hochreiter, S., & Schmidhuber, J. (1999). Feature extraction through LOCOCODE. Neural Computation, 11, 679–714.CrossRefGoogle ScholarPubMed

[Holmstrom & Bjorkman, 1999] Holmstrom, K., & Bjorkman, M. (1999). The TOMLAB NLPLIB. Advanced Modeling and Optimization, 1, 70–86. Available from http://www.ima.mdh.se/tom/.Google Scholar

[Horn & Johnson, 1985] Horn, R.A., & Johnson, C.R. (1985). Matrix Analysis. Cambridge University Press.CrossRefGoogle Scholar

[Huang et al, 1998] Huang, M., Leahy, R.M., Mosher, J.C., & Spencer, M.E. (1998). Comparing the source localization accuracy of EEG and MEG for different head modeling techniques using a human skull phantom. International Journal of Psychophysiology, 30, 200.CrossRefGoogle Scholar

[Huber, 1985] Huber, P. (1985). Projection pursuit. Annals of Statistics, 13(2), 435–475.Google Scholar

[Hyvärinen, 1997] Hyvärinen, A. (1997). One-unit contrast functions for independent component analysis: a statistical analysis. Pages 388-397 of: Proceedings of IEEE Workshop on Neural Networks for Signal Processing VII.Google Scholar

[Hyvärinen, 1998a] Hyvärinen, A. (1998a). New approximations of differential entropy for independent component analysis and projection pursuit. Pages 273-279 of: Advances in Neural Information Processing Systems, vol. 10. MIT Press.Google Scholar

[Hyvärinen, 1998b] Hyvärinen, A. (1998b). The Fast IC A MATLAB toolbox. Helsinki Univ. of Technology. Available at http://www.eis.hut.fi/projects/ica/fastica/.Google Scholar

[Hyvärinen, 1999a] Hyvärinen, A. (1999a). Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3), 626–634.CrossRefGoogle ScholarPubMed

[Hyvärinen, 1999b] Hyvärinen, A. (1999b). The fixed-point algorithm and maximum likelihood estimation for independent component analysis. Neural Processing Letters, 10(1), 1–5.CrossRefGoogle Scholar

[Hyvärinen, 1999c] Hyvärinen, A. (1999c). Gaussian moments for noisy independent component analysis. IEEE Signal Processing Letters, 6(6), 145-147.CrossRefGoogle Scholar

[Hyvärinen, 1999d] Hyvärinen, A. (1999d). Survey on independent component analysis. Neural Computing Surveys, 2, 94–128. Available from http://www.icsi.berkeley.edu/~jagota/NCS.Google Scholar

[Hyvärinen & Oja, 1997] Hyvärinen, A., & Oja, E. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7), 1483-1492.CrossRefGoogle Scholar

[Hyvärinen & Oja, 1998] Hyvärinen, A., & Oja, E. (1998). Independent component analysis by general nonlinear Hebbian-like learning rules. Signal Processing, 64(3), 301–313.CrossRefGoogle Scholar

[Hyvärinen & Pajunen, 1999] Hyvärinen, A., & Pajunen, P. (1999). Nonlinear independent component analysis: existence and uniqueness results. Neural Networks, 12, 209–219.CrossRefGoogle ScholarPubMed

[Hyvärinen et al, 1999a] Hyvärinen, A., Cristescu, R., & Oja, E. (1999a). A fast algorithm for estimating overcomplete ICA bases for image windows. Pages 894-899 of: Proceedings of International Joint Conference on Neural Networks.Google Scholar

[Hyvärinen et al, 1999b] Hyvärinen, A., Hoyer, P., & Oja, E. (1999b). Sparse code shrinkage: denoising by nonlinear maximum likelihood estimation. Pages 473–479 of: Kearns, M.S., Solla, S. A., & Cohn, D.A. (eds), Advances in Neural Information Processing Systems, vol. 11. MIT Press.Google Scholar

[Hyvärinen et al, 1999c] Hyvärinen, A., Särelä, J., & Vigário, R. (1999). Spikes and bumps: artefacts generated by independent component analysis with insufficient sample size. Pages 425-430 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Hyvärinen et al, 2000] Hyvärinen, A., Hoyer, P.O., & Inki, M. (2000). Topographic independent component analysis: Visualizing the independence structure. Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000).Google Scholar

[Ikram & Morgan, 2000] Ikram, M., & Morgan, D. (2000). Exploring permutation inconsistency in blind separation of speech signals in a reverberant environment. ICASSP 2000.Google Scholar

[Isard & Blake, 1996] Isard, M., & Blake, A. (1996). Contour tracking by stochastic density propagation of conditional density. Pages 343-356 of: Proceedings of European Conference Computer Vision.Google Scholar

[Isbell & Viola, 1999] Isbell, B.L., & Viola, P. (1999). Restructuring sparse high dimensional data for effective retrieval. Pages 480–486 of: Kearns, M.S., Solla, S. A., & Cohn, D.A. (eds), Advances in Neural Information Processing Systems, vol. 11. MIT Press.Google Scholar

[Jaakkola & Jordan, 1997] Jaakkola, T.S., & Jordan, M.I. (1997). Bayesian logistic regression: a variational approach. Pages 283-294 of: Proceedings of the 1997 Conference on Artificial Intelligence and Statistics.Google Scholar

[Jain & Dubes, 1988] Jain, A.K., & Dubes, R. (1988). Algorithms for Clustering Data. Prentice-Hall.Google Scholar

[Jänich, 1977] Jänich, K. (1977). Einführung in die Funktionentheorie. Springer.CrossRefGoogle Scholar

[Jazwinski, 1973] Jazwinski, A.H. (1973). Stochastic Processes and Filtering Theory. Academic Press.Google Scholar

[Jolliffe, 1986] Jolliffe, I.T. (1986). Principal Component Analysis. Springer.CrossRefGoogle Scholar

[Jones & Sibson, 1987] Jones, M., & Sibson, R. (1987). What is projection pursuit?Journal of the Royal Statistical Society, series A, 150, 1–36.CrossRefGoogle Scholar

[Jordan, 1999] Jordan, M.I. (ed). (1999). Learning in Graphical Models. MIT Press.Google Scholar

[Jung et al, 1998] Jung, T.-R., Humphries, C., Lee, T.-W., Makeig, S., McKeown, M.J., Iragui, V., & Sejnowski, T.J. (1998). Extended ICA removes artifacts from electroencephalographic recordings. Jordan, M.I., Kearns, M.J., & Solla, S.A. (eds), Advances in Neural Information Processing Systems, vol. 10. MIT Press.Google Scholar

[Jung et al., 1999a] Jung, T.-P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & Sejnowski, T.J. (1999a). Analyzing and visualizing single-trial event-related potentials. Pages 118-124 of: Kearns, M.S., Solla, S.A., & Cohn, D.A. (eds), Advances in Neural Information Processing Systems, vol. 11. MIT Press.Google Scholar

[Jung et al., 1999b] Jung, T.-R., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & Sejnowski, T.J. (1999b). Independent component analysis of single-trial event-related potentials. Pages 173-178 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Jung et al, 2000] Jung, T.-P., Humphries, C., Lee, T.-W., McKeown, M.J., Iragui, V., Makeig, S., & Sejnowski, T.J. (2000). Removing electro encephalographic artifacts by blind source separation. Psychophysiology, 37, 163–178.CrossRefGoogle Scholar

[Jutten & Herault, 1991] Jutten, C., & Herault, J. (1991). Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture. Signal Processing, 24, 1–10.CrossRefGoogle Scholar

[Kaiman & Bucy, 1961] Kaiman, R., & Bucy, R. (1961). New results in linear filtering and prediction theory. Journal of Basic Engineering, Transactions of ASME series D, 83(95-108).Google Scholar

[Karhunen & Joutsensalo, 1994] Karhunen, J., & Joutsensalo, J. (1994). Representation and separation of signals using nonlinear PCA type learning. Neural Networks, 7, 113–127.CrossRefGoogle Scholar

[Karhunen & Malaroiu, 1999a] Karhunen, J., & Malaroiu, S. (1999a). Local independent component analysis using clustering. Pages 43-48 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Karhunen & Malaroiu, 1999b] Karhunen, J., & Malaroiu, S. (1999b). Locally linear independent component analysis. Pages 882-887 of: Proceedings of the International Joint Conference on Neural Networks (IJCNN'99).Google Scholar

[Kass et al, 1991] Kass, R.E., Tierney, L., & Kadane, J.B. (1991). Laplace's method in Bayesian analysis. Contemporary Mathematics, 115, 89–99.Google Scholar

[Kawamoto et al, 1998] Kawamoto, M., Matsuoka, K., & Ohnishi, N. (1998). A method of blind separation for convolved non-stationary signals. Neurocomputing, 22, 157–171.CrossRefGoogle Scholar

[Kendal & Stuart, 1969] Kendal, M.G., & Stuart, A. (1969). The Advanced Theory of Statistics. Charles Griffin.Google Scholar

[Kirby & Sirovich, 1990] Kirby, M., & Sirovich, L. (1990). Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(1), 103–108.CrossRefGoogle Scholar

[Kisilev et al, 2000] Kisilev, P., Zibulevsky, M., Zeevi, Y.Y., & Pearlmutter, B.A. (2000). Multiresolution framework for sparse blind source separation. Tech. rept. Technion Israel Institute of Technology.Google Scholar

[Kitagawa, 1996] Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5, 1–25.Google Scholar

[Kiviluoto & Oja, 1998] Kiviluoto, K., & Oja, E. (1998). Independent Component Analysis for parallel economic time series. Pages 895-898 of: Proceedings of ICONIP'98, vol. 2.Google Scholar

[Knuth, 1998a] Knuth, K.H. (1998a). Bayesian source separation and localization. Pages 147-158 of: Mohammad-Djafari, A. (ed), SPIE'98 Proceedings: Bayesian Inference for Inverse Problems.Google Scholar

[Knuth, 1998b] Knuth, K.H. (1998b). Difficulties applying recent blind source separation techniques to EEG and MEG. Pages 209-222 of: Rychert, J.T., & Smith, C.R. (eds), Maximum Entropy and Bayesian Methods. Kluwer.Google Scholar

[Knuth, 1999] Knuth, K.H. (1999). A Bayesian approach to source separation. Pages 283-288 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Koehler & Orglmeister, 1999] Koehler, B.-U., & Orglmeister, R. (1999). Independent component analysis using autoregressive models. Pages 359-364 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Kohonen, 1995] Kohonen, T. (1995). Self-Organizing Maps. Springer.CrossRefGoogle Scholar

[Kolenda et al, 2000] Kolenda, T., Hansen, L.K., & Sigurdsson, S. (2000). Independent component analysis in text. Pages 236–256 of: Girolami, M. (ed), Advances in Independent Component Analysis. Perspectives in Neural Computing. Springer.Google Scholar

[Kowalski et al, 1996] Kowalski, N., Depireux, D.A., & Shamma, S.A. (1996). Analysis of dynamic spectra in ferret primary auditory cortex: I. Characteristics of single unit responses to moving ripple spectra. Journal of Neurophysiology, 76(5), 3503–3523.Google ScholarPubMed

[Landatter & Harshman, 1990] Landatter, T.K., & Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of American Society for Information Science, 41(391-407).Google Scholar

[Lappalainen, 1999] Lappalainen, H. (1999). Ensemble learning for independent component analysis. Pages 7-12 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Lappalainen & Honkela, 2000] Lappalainen, H., & Honkela, A. (2000). Bayesian nonlinear independent component analysis by multi-layer perceptrons. Pages 93-121 of: Girolami, M. (ed), Advances in Independent Component Analysis. Springer.Google Scholar

[Lappalainen & Miskin, 2000] Lappalainen, H., & Miskin, J. (2000). Ensemble learning. Pages 73-92 of: Girolami, M. (ed), Advances in Independent Component Analysis. Springer.Google Scholar

[Lappalainen et al, 2000a] Lappalainen, H., Giannakopoulos, X., Honkela, A., & Karhunen, J. (2000a). Nonlinear independent component analysis using ensemble learning: experiments and discussion. Proceedings of the 2nd International Workshop on Independent Component Analysis and Blind Signal Separation.Google Scholar

[Lappalainen et al., 2000b] Lappalainen, H., Giannakopoulos, X., Honkela, A., & Karhunen, J. (2000b). Nonlinear independent component analysis using ensemble learning: theory. Proceedings of the 2nd International Workshop on Independent Component Analysis and Blind Signal Separation.Google Scholar

[Lee, 1998] Lee, T.-W. (1998). Independent Component Analysis – Theory and Applications. Kluwer.CrossRefGoogle Scholar

[Lee & Lewicki, 2000] Lee, T.-W., & Lewicki, M.S. (2000). The generalized Gaussian mixture model using ICA. Proceedings of the 2nd international workshop on ICA.Google Scholar

[Lee & Seung, 1999] Lee, D.D., & Seung, H.S. (1999). Learning the parts of objects by non-negative matrix factorisation. Nature, 401, 788–791.Google Scholar

[Lee et al, 1997] Lee, T.-W., Bell, A.J., & Lambert, R. (1997). Blind separation of delayed and convolved sources. Pages 758-764 of: Mozer, M.C., Jordan, M.I., & Petsche, T. (eds), Advances in Neural Information Processing Systems, vol. 9. MIT Press.Google Scholar

[Lee et al, 1998] Lee, T.-W., Lewicki, M.S., Girolami, M., & Sejnowski, T.J. (1998). Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Processing Letters.Google Scholar

[Lee et al, 1999a] Lee, T.-W., Lewicki, M.S., & Sejnowski, T.J. (1999a). ICA mixture models for unsupervised classification and automatic context switching. Pages 209-214 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Lee et al, 1999b] Lee, T.-W., Girolami, M., & Sejnowski, T.J. (1999b). Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural Computation, 11, 417–441.CrossRefGoogle Scholar

[Lee et al, 1999c] Lee, T.-W., Lewicki, M.S., & Sejnowski, T.J. (1999c). Unsupervised classification with non-Gaussian mixture models using ICA. Pages 508-514 of: Kearns, M.S., Solla, S. A., & Cohn, D.A. (eds), Advances in Neural Information Processing Systems, vol. 11. MIT Press.Google Scholar

[Lee et al, 2000a] Lee, D.D., Rokni, U., & Sompolinsky, H. (2000a). Algorithms for independent component analysis and higher order statistics. Advances in Neural Information Processing Systems, vol. 12. MIT Press.Google Scholar

[Lee et al, 2000b] Lee, T.-W., Girolami, M., Bell, A.J., & Sejnowski, T.J. (2000b). A unifying framework for independent component analysis. International Journal on Mathematical and Computer Models, 39(11), 1-21. Available from http://www.cnl. salk.edu/~tewon/Public/mcm.ps.gz.Google Scholar

[Lewicki, 2000] Lewicki, M.S. (2000). A flexible prior for independent component analysis. Neural Computation. (Submitted).Google Scholar

[Lewicki & Olshausen, 1998] Lewicki, M.S., & Olshausen, B. (1998). Inferring sparse, overcomplete image codes using an efficient coding framework. Pages 556-562 of: Jordan, M.I., Kearns, M.J., & Solla, S.A. (eds), Advances in Neural Information Processing Systems, vol. 10.Google Scholar

[Lewicki & Olshausen, 1999] Lewicki, M.S., & Olshausen, B.A. (1999). A probabilistic framework for the adaptation and comparison of image codes. Journal of the Optical Society of America A, 16(7), 1587–1601.CrossRefGoogle Scholar

[Lewicki & Sejnowski, 1998] Lewicki, M.S., & Sejnowski, T.J. (1998). Learning nonlinear overcomplete representations for efficient coding. Pages 815–821 of: Jordan, M.I., Kearns, M.J., & Solla, S.A. (eds), Advances in Neural Information Processing Systems 10, vol. 10.Google Scholar

[Lewicki & Sejnowski, 2000] Lewicki, M.S., & Sejnowski, T.J. (2000). Learning overcomplete representations. Neural Computation, 12(2), 337-365.CrossRefGoogle ScholarPubMed

[Liavas & Regalia, 1998] Liavas, A.P., & Regalia, P.A. (1998). Acoustic echo cancellation: do IIR models offer better modeling capabilities than their FIR counterparts?IEEE Transactions on Signal Processing, 46, 2499–2504.CrossRefGoogle Scholar

[Lin, 1999] Lin, J.K. (1999). Factorizing probability density functions: generalizing ICA. Pages 313-318 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Lin et al, 1997] Lin, J.K., Grier, D., & Cowan, J. (1997). Faithful representation of separable distributions. Neural Computation, 9, 1305–1320.CrossRefGoogle Scholar

[Linsker, 1989] Linsker, R. (1989). An application of the principle of maximum information transfer to linear systems. Touretzky, D.S. (ed), Advances in Neural Information Processing Systems, vol. 1. Morgan Kaufmann.Google Scholar

[Linsker, 1992] Linsker, R. (1992). Local synaptic learning rules suffice to maximise mutual information in a linear network. Neural Computation, 4, 691–702.CrossRefGoogle Scholar

[MacKay, 1994] MacKay, D.J.C. (1994). Bayesian non-linear modelling for the energy prediction competition. ASHRAE Transactions, 100, 1053–1062.Google Scholar

[MacKay, 1995] MacKay, D.J.C. (1995). Developments in probabilistic modelling with neural networks – ensemble learning. Pages 191-198 of: Neural Networks: Artificial Intelligence and Industrial Applications. Proceedings of the 3rd Annual Symposium on Neural Networks.Google Scholar

[MacKay, 1996] MacKay, D.J.C. (1996). Maximum likelihood and covariant algorithms for independent component analysis. Tech. rept. University of Cambridge. Available from http://wol.ra.phy.cam.ac.uk/mackay/.Google Scholar

[MacKay, 1999] MacKay, D.J.C. (1999). Monte Carlo methods. Pages 175-204 of: Jordan, M.I. (ed), Learning in Graphical Models. Kluwer.Google Scholar

[Makeig, 1999] Makeig, S. (1999). ICA/EEG toolbox. Computational Neurobiology Laboratory, The Salk Institute. Available from http://www.cnl.salk.edu/~tewon/ica_cnl.html.Google Scholar

[Makeig et al, 1996] Makeig, S., Bell, A.J., Jung, T.-P., & Sejnowski, T.J. (1996). Independent component analysis of electroencephalographic data. Pages 145–151 of: Touretzky, D., Mozer, M., & Hasselmo, M. (eds), Advances in Neural Information Processing Systems, vol. 8. MIT Press.Google Scholar

[Makeig et al, 1997a] Makeig, S., Jung, T.-P., Bell, A.J., Ghahremani, D., & Sejnowski, T.J. (1997a). Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences, 94, 10979–84.CrossRefGoogle ScholarPubMed

[Makeig et al, 1997b] Makeig, S., Jung, T-P., Bell, A., Ghahremani, D., & Sejnowski, T.J. (1997b). Transiently time-locked fMRI activations revealed by independent components analysis. Proceedings of the National Academy of Sciences, 95, 803–810.Google Scholar

[Makeig et al, 1999] Makeig, S., Westerfield, M., Townsend, J., Jung, T.-R., Courchesne, E., & Sejnowski, T.J. (1999). Functionally independent components of early event-related potentials in a visual spatial attention task. Philosophical Transactions of the Royal Society: Biological Sciences, 354, 1135–1144.Google Scholar

[Mallat, 1998] Mallat, S. (1998). A Wavelet Tour of Signal Processing. Academic Press.Google Scholar

[Manduchi & Portilla, 1999] Manduchi, R., & Portilla, J. (1999). Independent component analysis of textures. IEEE International Conference on Computer Vision.Google Scholar

[Matsuoka et al, 1995] Matsuoka, K., Ohya, M., & Kawamoto, M. (1995). A neural net for blind separation of nonstationary signals. Neural Networks, 8(3), 411–419.CrossRefGoogle Scholar

[McCallum & Nigam, 1998] McCallum, A., & Nigam, K. (1998). A comparison of event models for naive Bayes text classification. AAAI-98 Workshop on Learning for Text Categorisation. Available from http://www.cs.emu.edu/~mccallum.Google Scholar

[McCullagh & Nelder, 1983] McCullagh, P., & Nelder, J.A. (1983). Generalized Linear Models. Chapman and Hall.CrossRefGoogle Scholar

[McKeown et al, 1998a] McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.-P., Kindermann, S.S., Bell, A.J., & Sejnowski, T.J. (1998a). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6, 160–188.Google ScholarPubMed

[McKeown et al, 1998b] McKeown, M.J., Jung, T.-P., Makeig, S., Brown, G.G., Kindermann, G., Lee, T.-W., & Sejnowski, T.J. (1998b). Spatially independent activity patterns in functional magnetic resonance imaging data during the Stroop color-naming task. Proceedings of the National Academy of Sciences, 95, 803–810.CrossRefGoogle ScholarPubMed

[Minka, 2000] Minka, T.P. (2000). Automatic choice of dimensionality for PC A. Tech. rept. 514. MIT. Available from ftp://whitechapel.media.mit.edu/pub/tech-reports/.Google Scholar

[Mohammad-Djafari, 1999] Mohammad-Djafari, A. (1999). A Bayesian approach to source separation. 19th International Workshop on Maximum Entropy and Bayesian Methods (MaxEnt99).Google Scholar

[Molgedey & Schuster, 1994] Molgedey, L., & Schuster, H.G. (1994). Separation of a mixture of independent signals using time delayed correlations. Physical Review Letters, 72(23), 3634–3637.CrossRefGoogle ScholarPubMed

[Moreau & Macchi, 1993] Moreau, E., & Macchi, O. (1993). New self-adaptive algorithms for source separation based on contrast functions. Pages 215-219 of: Proceedings of IEEE Signal Processing Workshop on Higher Order Statistics.Google Scholar

[Moulines et al, 1997] Moulines, E., Cardoso, J.-F., & Gassiat, E. (1997). Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models. Pages 3617-20 of: Proceedings of ICASSP'97, vol. 5.Google Scholar

[Murata et al, 1997] Murata, N., Ikeda, S., & Ziehe, A. (1997). Adaptive on-line learning in changing environments. Pages 599–605 of: Mozer, M.C., Jordan, M.I., & Petsche, T. (eds), Advances in Neural Information Processing Systems, vol. 9. MIT Press.Google Scholar

[Nadal & Parga, 1994] Nadal, J.-P., & Parga, N. (1994). Non-linear neurons in the low noise limit: a factorial code maximises information transfer. Network, 5, 565 581.CrossRefGoogle Scholar

[Neal & Hinton, 1993] Neal, R.M., & Hinton, G.E. (1993). A view of the EM algorithm that justifies incremental, sparse, and other variants. Pages 355–368 of: Jordan, M.I. (ed), Learning in Graphical Models. Kluwer.Google Scholar

[Ochs et al, 1999] Ochs, M., Stoyanova, R., Arias-Mendoza, F., & Brown, T. (1999). A new method for spectral decomposition using a bilinear Bayesian approach. Journal of Magnetic Resonance, 137, 161–176.CrossRefGoogle ScholarPubMed

[Oja & Kaski, 1999] Oja, E., & Kaski, S. (eds). (1999). Kohonen maps. Elsevier.Google Scholar

[Oja et al, 1997] Oja, E., Karhunen, J, Hyvärinen, A., Vigário, R., & Hurri, J. (1997). Neural independent component analysis – approaches and applications. Pages 167–188 of: Amari, S.-I., & Kasabov, N. (eds), Brain-Like Computing and Intelligent Information Systems. Springer.Google Scholar

[Olshausen & Field, 1996] Olshausen, B., & Field, D. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607–609.CrossRefGoogle ScholarPubMed

[Olshausen & Field, 1997] Olshausen, B.A., & Field, D.J. (1997). Sparse coding with an overcomplete basis set: a strategy employed by VI?Vision Research, 37, 3311–3325.CrossRefGoogle Scholar

[O'Ruanaidth & Fitzgerald, 1996] O'Ruanaidth, J.J.K., & Fitzgerald, W.J. (1996). Numerical Bayesian Methods Applied to Signal Processing. Springer.CrossRefGoogle Scholar

[O'Toole et al, 1991a] O'Toole, A.J., Abdi, H., Deffenbacher, K.A., & Bartlett, J.C. (1991a). Classifying faces by race and sex using an autoassiciative memory trained for recognition. Pages 847–851 of: Hammond, K.J., & Gentner, D. (eds), Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum.Google Scholar

[O'Toole et al, 1991b] O'Toole, A.J., Deffenbacher, K.A., Abdi, H., & Bartlett, J.C. (1991b). Simulating the “other-race effect” as a problem in perceptual learning. Connection Science, 3(2), 163–178.CrossRefGoogle Scholar

[Pajunen & Girolami, 2000] Pajunen, P., & Girolami, M. (2000). Implementing decisions in binary decision trees using independent component analysis. Second International Workshop on Independent Component Analysis (ICA2000).Google Scholar

[Pajunen & Karhunen, 1997] Pajunen, P., & Karhunen, J. (1997). A maximum likelihood approach to nonlinear blind separation. Pages 541-546 of: Proceedings of 1997 International Conference on Artificial Neural Networks.Google Scholar

[Pajunen et al, 1996] Pajunen, P., Hyvärinen, A, & Karhunen, J. (1996). Non-linear blind source separation by self-organizing maps. Pages 1207-1210 of: International Conference on Neural Information Processing. Springer.Google Scholar

[Papoulis, 1991] Papoulis, A. (1991). Probability, Random Variables and Stochastic Processes. McGraw-Hill.Google Scholar

[Parra, 1998a] Parra, L. (1998a). An Introduction to Independent Component Analysis and Blind Source Separation. Tech. rept. Princeton University. Available from http://www.humanism.org/~lucas/.Google Scholar

[Parra, 1998b] Parra, L. (1998b). Blind source separation based on multiple decorrelations. http://www.sarnoff.com/career_move/tech_papers/BSS.html.CrossRefGoogle Scholar

[Parra & Spence, 2000a] Parra, L, & Spence, C. (2000a). Convolutive blind source separation of non-stationary sources. IEEE Transactions on Signal Processing, 320-327.Google Scholar

[Parra & Spence, 2000b] Parra, L, & Spence, C. (2000b). On-line convolutive blind source separation of non-stationary sources. VLSI Signal Processing Journal, 26(1), 39–16.CrossRefGoogle Scholar

[Parra et al, 1995] Parra, L. C., Deco, G., & Miesbach, S. (1995). Redundancy reduction with information-preserving maps. Network: Computation in Neural Systems, 6, 61–72.CrossRefGoogle Scholar

[Parra et al, 1996] Parra, L., Deco, G., & Miesbach, S. (1996). Statistical independence and novelty detection with information-preserving nonlinear maps. Neural Computation, 8, 260–269.CrossRefGoogle Scholar

[Parra et al, 2000] Parra, L., Spence, C., Sajda, P., Ziehe, A., & Millier, K.-R. (2000). Unmixing hyperspectral data. Advances in Neural Information Processing Systems, vol. 12.Google Scholar

[Pearlmutter & Parra, 1996] Pearlmutter, B.A., & Parra, L.C. (1996). A context-sensitive generalization of ICA. Pages 151-157 of: International Conference on Neural Information Processing (ICONIP'96). Springer. Available from http://www.cs.unm.edu/~bap/papers/iconip-96-cica.ps.gz.Google Scholar

[Pearlmutter & Parra, 1997] Pearlmutter, B.A., & Parra, L.C. (1997). Maximum likelihood blind source separation: a context-sensitive generalization of ICA. Pages 613–619 of: Mozer, M.C., Jordan, M.I., & Petsche, T. (eds), Advances in Neural Information Processing Systems, vol. 9. MIT Press.Google Scholar

[Pearson, 1894] Pearson, K. (1894). Contributions to the mathematical study of evolution. Philosophical Transations of the Royal Society, series A, 185(71).Google Scholar

[Pearson, 1901] Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 6, 559.Google Scholar

[Penny et al, 2000] Penny, W.D., Everson, R.M., & Roberts, S.J. (2000). Hidden Markov independent components analysis. Pages 1–22 of: Girolami, M. (ed), Advances in Independent Component Analysis. Springer.Google Scholar

[Pham, 1996] Pham, D.-T. (1996). Blind separation of instantaneous mixture of sources via an independent component analysis. IEEE Transactions on Signal Processing, 44(11), 2668–2779.Google Scholar

[Pham, 1999] Pham, D.-T. (1999). Joint approximate diagonalization of positive definite Hermitian matrices. Tech. rept. Laboratoire de Modélisation et de Calcul. Submitted to Simax. Available from http://www-lmc.imag.fr/lmc-sms/Dinh-Tuan.Pham/jadiag/.Google Scholar

[Pham & Cardoso, 2000a] Pham, D.-T., & Cardoso, J.-F. (2000a). A Cramér-Rao hound for the separation of non-stationary non-Gaussian sources. Available at http://tsi.enst.fr/~cardoso/CRBnSnG.ps and http://www-lmc/lmc-sms/Dinh-Tuan.Pham/BBS/CRBnSnG.ps.Google Scholar

[Pham & Cardoso, 2000b] Pham, D.-T., & Cardoso, J.-F. (2000b). Blind separation of instantaneous mixtures of non-stationary sources. Proceedings of 2nd International Conference on Independent Component Analysis and Blind Source Separation, ICA 2000.Google Scholar

[Pham & Garrat, 1993] Pham, D.-T., & Garrat, P. (1993). Séparation aveugle de sources temporellement corrélées. Pages 317-320 of: Proceedings of Gretsi.Google Scholar

[Pham & Garrat, 1997] Pham, D.-T., & Garrat, P. (1997). Blind separation of mixture of independent sources through a quasi-maximum-likelihood approach. IEEE Transactions on Signal Processing, 45(7), 1712–1725.Google Scholar

[Pham et al., 1992] Pham, D.T., Garrat, P., & Jutten, C. (1992). Separation of a mixture of independent sources through a maximum likelihood approach. Pages 771-774 of: European Signal Processing Conference.Google Scholar

[Pinter, 1996] Pinter, I. (1996). Perceptual wavelet-representation of speech signals and its application to speech enhancement. Computer Speech and Language, 10, 1–22.CrossRefGoogle Scholar

[Porrill et al, 2000] Porrill, J., Stone, J.V., Berwick, J., Mayhew, J., & Coffey, P. (2000). Analysis of optical imaging data using weak models and ICA. Pages 217–233 of: Girolami, M. (ed), Advances in Independent Component Analysis. Perspectives in Neural Computing. Springer.Google Scholar

[Press et al, 1992] Press, W.H., Teukolsky, S.A., Vetterling, W.T., & Flannery, B.P. (1992). Numerical recipes in C. 2nd edn. Cambridge University Press.Google Scholar

[Quinlan, 1986] Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1, 81–106.CrossRefGoogle Scholar

[Rabiner & Juang, 1993] Rabiner, L., & Juang, B.-H. (1993). Fundamentals of Speech Recognition. Prentice-Hall.Google Scholar

[Rajan & Rayner, 1997] Rajan, J.J., & Rayner, P.J.W. (1997). Model order selection for the singular value decomposition and the discrete Karhunen-Loeve transform using a Bayesian approach. IEE Proceedings on Vision, Images and Signal Processing, 144(2), 116–123.Google Scholar

[Rangaswamy et al., 1993] Rangaswamy, M., Weiner, D., & Oeztuerk, A. (1993). Non-Gaussian random vector identification using spherically invariant random processes. IEEE Transactions on Aerospace and Electronic Systems, 29(1), 111–123.CrossRefGoogle Scholar

[Richardson & Green, 1997] Richardson, S, & Green, P.J. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society, series B), 59(4), 731–758.Google Scholar

[Rissanen, 1978] Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14, 465–471.CrossRefGoogle Scholar

[Ristaniemi & Joutsensalo, 1999] Ristaniemi, T., & Joutsensalo, J. (1999). On the performance of blind source separation in CDMA downlink. Pages 437-442 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Roberts, 1998] Roberts, S.J. (1998). Independent component analysis: source assessment and separation, a Bayesian approach. IEE Proceedings, Vision, Image and Signal Processing, 145(3), 149–154.CrossRefGoogle Scholar

[Roberts et al, 1999] Roberts, S.J., Everson, R.M., & Rezek, I. (1999). Minimum entropy data partitioning. Pages 844-849 of: Proceedings of the International Conference on Artificial Neural Networks, vol. 2.Google Scholar

[Roth & Baram, 1996] Roth, Z., & Baram, Y. (1996). Multidimensional density by shaping sigmoids. IEEE Transactions on Neural Networks, 7(5), 1291–1298.CrossRefGoogle ScholarPubMed

[Roweis, 1997] Roweis, S. (1997). EM algorithms for PCA and SPCA. Pages 626–632 of: Jordan, M.I., Kearns, M.J., & Solla, S.A. (eds), Advances in Neural Information Processing Systems, vol. 10.Google Scholar

[Roweis & Ghahramani, 1999] Roweis, S., & Ghahramani, Z. (1999). A unifying review of linear Gaussian models. Neural Computation, 11(2), 305–346.CrossRefGoogle ScholarPubMed

[Rubin & Thayer, 1982] Rubin, D, & Thayer, D. (1982). EM algorithms for ML factor analysis. Psychometrica, 47, 69–76.Google Scholar

[Ruderman, 1998] Ruderman, D.L. (1998). Origins of scaling in natural images. Vision Research, 37(23), 3385–3398.Google Scholar

[Rupp, 1993] Rupp, Markus. (1993). The behavior of LMS and NLMS algorithms in the presence of spherically invariant processes. IEEE Transactions on Signal Processing, 41(3), 1149–1160.

[Saul & Jordan, 1996] Saul, L., & Jordan, M.I. (1996). Exploiting tractable structures in intractable networks. Touretzky, D., Mozer, M, & Hasselmo, M. (eds), Advances in Neural Information Processing Systems, vol. 8. MIT Press.Google Scholar

[Saul et al, 1996] Saul, L.K., Jaakkola, T., & Jordan, M.I. (1996). Mean field theory of sigmoid belief networks. Journal of Artificial Intelligence Research, 4, 61–76.Google Scholar

[Schießl et al., 1999] Schießl, I., Stetter, M., Mayhew, J.E.W., Askew, S., McLoughlin, N., Levitt, J.B., Lund, J.S., & Obermayer, K. (1999). Blind Separation of spatial signal patterns from optical imaging records. Pages 179-184 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Schießl et al, 2000] Schießl, I., Stetter, M., Mayhew, J.E.W., McLoughlin, N., Lund, J.S., & Obermayer, K. (2000). Blind signal separation from optical imaging recordings with extended spatial decorrelation. IEEE Transactions on Biomedical Engineering, 47.CrossRefGoogle ScholarPubMed

[Schoebben, 1998] Schoebben, D. (1998). Real room recordings and separation results, http://www.esp.ele.tue.nl/onderzoek/daniels/BSS.html.Google Scholar

[Schöner et al, 2000] Schöner, H., Stetter, M., Schießl, I., Mayhew, J.E.W., Lund, J.S., McLoughlin, N., & Obermayer, K. (2000). Application of blind separation of sources to optical recording of brain activity. Advances in Neural Information Processing Systems, vol. 12.Google Scholar

[Shalvi & Weinstein, 1993] Shalvi, O, & Weinstein, E. (1993). Super-exponential methods for blind deconvolution. IEEE Transactions on Information Theory, 39(2), 504–519.CrossRefGoogle Scholar

[Shalvi & Weinstein, 1994] Shalvi, O., & Weinstein, E. (1994). Universal methods for blind deconvolution. Pages 8–59 of: Haykin, S. (ed), Blind Deconvolution. Prentice-Hall.Google Scholar

[Sirovich, 1987] Sirovich, L. (1987). Turbulence and the dynamics of coherent structures. Quarterly of Applied Mathematics, XLV(3), 561-590.Google Scholar

[Sirovich & Everson, 1992] Sirovich, L, & Everson, R.M. (1992). Analysis and management of large scientific databases. International Journal of Supercomputing Applications, 6(1), 50-68.CrossRefGoogle Scholar

[Sirovich & Kirby, 1987] Sirovich, L, & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America, 4A(3), 519–524.Google Scholar

[Sivia, 1996] Sivia, D.S. (1996). Data Analysis: a Bayesian Tutorial. Clarendon.Google Scholar

[Souloumiac, 1995] Souloumiac, A. (1995). Blind source detection and separation using second order non-stationarity. Pages 1912-1915 of: International Conference on Acoustics, Speech and Signal Processing.Google Scholar

[Spence & Parra, 2000] Spence, C., & Parra, L. (2000). Hierarchical image probability (HIP) model. Advances in Neural Information Processing Systems, vol. 12. MIT Press.Google Scholar

[Stone et al., 1999] Stone, J.V., Porrill, J., Büchel, C., & Friston, K. (1999). Spatial, temporal and spatiotemporal independent component analysis of fMRI data. Proceedings of the Conference on Spatio-temporal Modelling and its Applications, University of Leeds, UK.Google Scholar

[Taleb & Jutten, 1999] Taleb, A., & Jutten, C. (1999). Source separation in post-nonlinear mixtures. IEEE Transactions on Signal Processing, 47, 2807–2820.CrossRefGoogle Scholar

[Tang et al, 1999] Tang, A.C., Pearlmutter, B.A., & Zibulevsky, M. (1999). Blind separation of neuromagnetic responses. Computational Neuroscience. In press as a special issue of Neurocomputing.Google Scholar

[Tang et al., 2000] Tang, A.C., Pearlmutter, B.A., Zibulevsky, M., Hely, T.A., & Weisend, M.P. (2000). An MEG study of response latency and variability in the human visual system during a visual-motor integration task. Pages 185-191 of: Advances in Neural Information Processing Systems, vol. 12. MIT Press.Google Scholar

[Theunissen & Doupe, 1998] Theunissen, F.E., & Doupe, AJ. (1998). Temporal and spectral sensitivity of auditory neurons in the nucleus HVc of male zebra finches. Journal of Neuroscience, 18(10), 3786–3802.CrossRefGoogle ScholarPubMed

[Thi & Jutten, 1995] Thi, H.-L.N., & Jutten, C. (1995). Blind source separation for convolutive mixtures. Signal Processing, 45(2), 209–229.CrossRefGoogle Scholar

[Tipping, 1999] Tipping, M.E. (1999). Probabilistic visualisation of high-dimensional binary data. Pages 592–598 of: Kearns, M.S., Solla, S. A., & Cohn, D.A. (eds), Advances in Neural Information Processing Systems, vol. 11. MIT Press.Google Scholar

[Tipping & Bishop, 1997] Tipping, M.E., & Bishop, C.M. (1997). Probabilistic Principal Component Analysis. Tech. rept. NCRG/97/010. Neural Computing Research Group, Aston University. Available from http://www.ncrg.aston.ac.uk.Google Scholar

[Tipping & Bishop, 1999] Tipping, M.E., & Bishop, C.M. (1999). Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2), 443–482.CrossRefGoogle ScholarPubMed

[Tong & Liu, 1990] Tong, L., & Liu, R. (1990). Blind estimation of correlated source signals. Proceeding of the Asilomar Conference.Google Scholar

[Torkkola, 1996a] Torkkola, K. (1996a). Blind separation of convolved sources based on information maximization. Pages 2097-2100 of: Proceedings of ICASSP.Google Scholar

[Torkkola, 1996b] Torkkola, K. (1996b). Blind separation of convolved sources based on information maximization. Neural Networks for Signal Processing, vol. VI. IEEE.Google Scholar

[Torkkola, 1996c] Torkkola, K. (1996c). Blind separation of delayed sources based on information maximization. Pages 423-432 of: IEEE Workshop on Neural Networks for Signal Processing.Google Scholar

[Torkkola, 1999] Torkkola, K. (1999). Blind separation of audio signals: are we there yet?Pages 239-244 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99. Available from http://members.home.net/torkkola/bss.html.Google Scholar

[Tsatsanis & Kweon, 1998] Tsatsanis, M.K., & Kweon, C. (1998). Source separation using second order statistics: identifiability conditions and algorithms. Pages 1574-1578 of: Proceedings of the 32nd Asilomar Conference on Signals, Systems, and Computers. IEEE.Google Scholar

[T'so et al, 1990] T'so, D., Frostig, R.D., Lieke, E.E., & Grinvald, A. (1990). Functional organisation of primate visual cortex revealed by high resolution optical imaging. Science, 249, 417–120.Google Scholar

[van Gerven & van Compernolle, 1995] van Gerven, S., & van Compernolle, D. (1995). Signal separation by symmetric adaptive decorrelation: Stability, convergence, and uniqueness. IEEE Transactions on Signal Processing, 43(7), 1602–1612.CrossRefGoogle Scholar

[van Hateren & Ruderman, 1998] van Hateren, J.H., & Ruderman, D.L. (1998). Independent component analysis of image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proceedings of the Royal Society of London, series B, 265, 2315–2320.Google ScholarPubMed

[van Hateren & van der Schaaf, 1998] van Hateren, J.H., & van der Schaaf, A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London, series B, 265, 359–366.Google ScholarPubMed

[Vetter et al, 1999] Vetter, R., Vesin, J.-M., Celka, P., & Scherrer, U. (1999). Observer of the autonomic cardiac outflow in humans using non-causal blind source separation. Pages 161-166 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Vigário, 1997] Vigário, R. (1997). Extraction of ocular artifacts from EEG using independent component analysis. Electroenceph. clin. NeurophysioL, 103(3), 395–104.CrossRefGoogle ScholarPubMed

[Vigário et al, 1999] Vigário, R., Sarela, J., Jousmaki, V., & Oja, E. (1999). Independent component analysis in decomposition of auditory and somatosensory evoked fields. Pages 167-172 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Vigário et al, 2000] Vigário, R., Sarela, J., Hamalainen, M., & Oja, E. (2000). Independent component approach to the analysis of EEG and MEG recordings. IEEE Transactions on Biomedical Engineering.Google Scholar

[Vinokourov & Girolami, 2000] Vinokourov, A., & Girolami, M. (2000). A probabilistic hierarchical clustering method for organising collections of text documents. Proceedings of 15th International Conference on Pattern Recognition.Google Scholar

[Wainwright & Simoncelli, 2000] Wainwright, M.J., & Simoncelli, E.P. (2000). Scale mixtures of Gaussians and the statistics of natural images. Advances in Neural Information Processing Systems, vol. 12. MIT Press.Google Scholar

[Wallace & Boulton, 1968] Wallace, C.S., & Boulton, D.M. (1968). An information measure for classification. Computer Journal, 11(2), 195–209.CrossRefGoogle Scholar

[Waterhouse et al, 1996] Waterhouse, S., Mackay, D.J.C., & Robinson, T. (1996). Bayesian methods for mixtures of experts. Touretzky, D., Mozer, M., & Hasselmo, M. (eds), Advances in Neural Information Processing Systems, vol. 8. MIT Press.Google Scholar

[Wax & Kailath, 1985] Wax, M., & Kailath, T. (1985). Detection of signals by information theoretic criteria. IEEE Transactions on Acoustics, Speech, and Signal Processing, 32, 387–392.Google Scholar

[Weinstein et al, 1993] Weinstein, E., Feder, M., & Oppenheim, A.V. (1993). Multi-channel signal separation by decorrelation. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1(4), 405–413.Google Scholar

[Weitzer et al, 1997] Weitzer, D., Stanhill, D., & Zeevi, Y.Y. (1997). Nonseparable two-dimensional multiwavelet transform for image coding and compression. Proceedings SPIE, 3309, 944–954.Google Scholar

[Wiibbeler et al, 2000] Wiibbeler, G., Ziehe, A., Mackert, B.-M., Müller, K.-R., Trahms, L., & Curio, G. (2000). Independent component analysis of non-invasively recorded cortical magnetic DC-fields in humans. IEEE Transactions on Biomedical Engineering, 47(5).Google Scholar

[Yang et al, 1998] Yang, H., Amari, S.-I., & Cichocki, A. (1998). Information-theoretic approach to blind separation of sources in non-linear mixture. Signal Processing, 64, 291–300.CrossRefGoogle Scholar

[Yen & Zhao, 1999] Yen, K.-C., & Zhao, Y. (1999). Adaptive co-channel speech separation and recognition. IEEE Transactions on Signal Processing, 7(2), 138–152.Google Scholar

[Ypma & Pajunen, 1999] Ypma, A., & Pajunen, P. (1999). Rotating machine vibration analysis with second-order independent component analysis. Pages 37-42 of: Proceedings of First International Conference on Independent Component Analysis and Blind Source Separation: ICA'99.Google Scholar

[Ziehe et al, 2000] Ziehe, A., Müller, K.-R., Nolte, G., Mackert, B.-M., & Curio, G. (2000). Artifact reduction in magnetoneurography based on time-delayed second-order correlations. EEE Trans, on Biomedical Eng, 47(1), 75–87.Google ScholarPubMed