Independent Component Analysis: Principles and Practice

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Overview

Independent Components Analysis (ICA) is an important tool for modelin g and understanding empirical datasets. Belonging to the class of gene ral linear models, it is a method of separating out independent source s from linearly mixed data. ICA provides a better decomposition than o ther well-known models such as principal component analysis. This self -contained book contains a structured series of edited papers by leadi ng researchers in the field and includes an extensive introduction to ICA. It reviews the major theoretical bases from a modern perspective, surveys current developments, and describes many case studies of appl ications in detail. Applications include biomedical examples, signal a nd image denoising, and mobile communications. The book discusses ICA within the framework of general linear models, but it also compares it to other paradigms such as neural network and graphical modeling meth ods.

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Editorial Reviews

From the Publisher
"...a highly technical book that is fascinating...There are a lot of deep ideas in this book and, as such, experts in ICA will want to have it at their disposal. No doubt this book would be a wonderful resource for a graduate student about to embark on the long pursuit of a thesis in the field." Technometrics
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Product Details

  • ISBN-13: 9780521792981
  • Publisher: Cambridge University Press
  • Publication date: 8/28/2010
  • Pages: 352
  • Product dimensions: 5.98 (w) x 9.02 (h) x 0.94 (d)

Table of Contents

1. Introduction Stephen Roberts and Richard Everson; 2. Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity Aapo Hyvärinen; 3. ICA, graphical models and variational methods Hagai Attias; 4. Nonlinear independent component analysis Juha Karhunen; 5. Separation of non-stationary natural signals Lucas Parra and Clay Spence; 6. Separation of non-stationary sources: algorithms and performance Jean-François Cardoso and Dinh-Tuan Pham; 7. Blind source separation by sparse decomposition in a signal dictionary Michael Zibulevsky, Barak Pearlmutter, Pau Bofill and Pavel Kisilev; 8. Ensemble learning for blind source separation James Miskin and David MacKay; 9. Image processing methods using ICA mixture models Te-Won Lee and Michael S. Lewicki; 10. Latent class and trait models for data classification and visualisation Mark Girolami; 11. Particle filters for non-stationary ICA Richard Everson and Stephen Roberts; 12. ICA: model order selection and dynamic source models William Penny, Stephen Roberts and Richard Everson.
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