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2327 results in Pattern Recognition and Machine Learning

Page 21 of 117

  • 39 - Decoding Hidden Markov Models

  • Ali H. Sayed, École Polytechnique Fédérale de Lausanne
  • Book:
    Inference and Learning from Data
    Published online:
    17 March 2023
    Print publication:
    22 December 2022, pp 1563-1608

  • Summary

    We continue our discussion of hidden Markov models (HMMs) and consider in this chapter the solution of decoding problems. Specifically, given a sequence of observations {yn,n=1,2,…,N}, we would like to devise mechanisms that allow us to estimate the underlying sequence of state or latent variables {zn,n=1,2,…,N}. That is, we would like to recover the state evolution that “most likely” explains the measurements. We already know how to perform decoding for the case of mixture models with independent observations by using (38.12a)–(38.12b). The solution is more challenging for HMMs because of the dependency among the states.

  • 37 - Latent Dirichlet Allocation

  • Ali H. Sayed, École Polytechnique Fédérale de Lausanne
  • Book:
    Inference and Learning from Data
    Published online:
    17 March 2023
    Print publication:
    22 December 2022, pp 1472-1516

  • Summary

    One prominent application of the variational inference methodology of Chapter 36 arises in the context of topic modeling. In this application, the objective is to discover similarities between texts or documents such as news articles. For example, given a large library of articles, running perhaps into the millions, such as a database of newspaper articles written over 100 years, it would be useful to be able to discover in an automated manner the multitude of topics that are covered in the database and to cluster together articles dealing with similar topics such as sports or health or politics. In another example, when a user is browsing an article online, it would be useful to be able to identify automatically the subject matter of the article in order to recommend to the reader other articles of similar content. Latent Dirichlet allocation (or LDA) refers to the procedure that results from applying variational inference techniques to topic modeling in order to address questions of this type.

Page 21 of 117