Independent Component Analysis: Principles and Practice

doi:10.1017/CBO9780511624148

Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field.

‘The book is intended to be a self-contained introduction and overview of this important development and it appears to meet the requirement admirably.’

Alex M. Andrew Source: Robotica

‘… is ideal for graduate students and researchers in the field.’

Source: Zentralblatt MATH

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Contents

Frontmatter
pp i-iv
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Contents
pp v-vi
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Preface
pp vii-x
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Contributors
pp xi-xii
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1 - Introduction
pp 1-70
- By S.J. Roberts, University of Oxford, R.M. Everson, University of Exeter
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2 - Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity
pp 71-94
- By Aapo Hyvärinen, Helsinki University of Technology
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3 - ICA, graphical models and variational methods
pp 95-112
- By H. Attias, Usa
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4 - Nonlinear ICA
pp 113-134
- By J. Karhunen, Helsinki University of Technology
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5 - Separation of non-stationary natural signals
pp 135-157
- By Lucas C. Parra, Usa, Clay D. Spence, Usa
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6 - Separation of non-stationary sources: algorithms and performance
pp 158-180
- By Jean-François Cardoso, France, Dinh-Tuan Pham, France
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7 - Blind source separation by sparse decomposition in a signal dictionary
pp 181-208
- By M. Zibulevsky, Technion Israel Institute of Technology, B.A. Pearlmutter, University of New Mexico, P. Bofill, Universitat Politècnica de Catalunya, P. Kisilev, Technion Israel Institute of Technology
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8 - Ensemble Learning for blind source separation
pp 209-233
- By J. W. Miskin, Uk, D.J.C. MacKay, Uk
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9 - Image processing methods using ICA mixture models
pp 234-253
- By T. W. Lee, Usa, M.S. Lewicki, Usa
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10 - Latent class and trait models for data classification and visualisation
pp 254-279
- By M. A. Girolami, University of Paisley
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11 - Particle filters for non-stationary ICA
pp 280-298
- By R.M. Everson, University of Exeter, S.J. Roberts, University of Oxford
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12 - ICA: model order selection and dynamic source models
pp 299-314
- By W.D. Penny, University of Oxford, S.J. Roberts, University of Oxford, R.M. Everson, University of Exeter
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References
pp 315-335
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Index
pp 336-338
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Independent Component Analysis

Principles and Practice

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Contents

Frontmatter
pp i-iv

Contents
pp v-vi

Preface
pp vii-x

Contributors
pp xi-xii

1 - Introduction
pp 1-70

2 - Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity
pp 71-94

3 - ICA, graphical models and variational methods
pp 95-112

4 - Nonlinear ICA
pp 113-134

5 - Separation of non-stationary natural signals
pp 135-157

6 - Separation of non-stationary sources: algorithms and performance
pp 158-180

7 - Blind source separation by sparse decomposition in a signal dictionary
pp 181-208

8 - Ensemble Learning for blind source separation
pp 209-233

9 - Image processing methods using ICA mixture models
pp 234-253

10 - Latent class and trait models for data classification and visualisation
pp 254-279

11 - Particle filters for non-stationary ICA
pp 280-298

12 - ICA: model order selection and dynamic source models
pp 299-314

References
pp 315-335

Index
pp 336-338

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Independent Component Analysis

Principles and Practice

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