Independent Component Analysis: Theory and Applications

Author:   Te-Won Lee
Publisher:   Springer
Edition:   1998 ed.
ISBN:  

9780792382614


Pages:   210
Publication Date:   31 October 1998
Format:   Hardback
Availability:   Out of print, replaced by POD   Availability explained
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Independent Component Analysis: Theory and Applications


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Overview

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. In the late 1990s, blind source separation by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, telecommunications, medical signal-processing and several data mining issues. This book presents theories and applications of ICA and includes examples of several real-world applications. Based on theories in probabilistic models, information theory and artificial neural networks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, nonlinear PCA, Bussgang algorithm and cumulant-based methods are reviewed and put in an information theoretic framework to unify several lines of ICA research. An algorithm is presented that is able to blindly separate mixed signals with sub- and super-Gaussian source distributions. The learning algorithms can be extended to filter systems, which allows the separation of voices recorded in a real environment (cocktail party problem). The ICA algorithm has been successfully applied to many biomedical signal-processing problems such as the analysis of electroencephalographic data and functional magnetic resonance imaging data. ICA applied to images results in independent image components that can be used as features in pattern classification problems such as visual lip-reading and face recognition systems. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification.

Full Product Details

Author:   Te-Won Lee
Publisher:   Springer
Imprint:   Springer
Edition:   1998 ed.
Dimensions:   Width: 15.50cm , Height: 1.50cm , Length: 23.50cm
Weight:   1.160kg
ISBN:  

9780792382614


ISBN 10:   0792382617
Pages:   210
Publication Date:   31 October 1998
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of print, replaced by POD   Availability explained
We will order this item for you from a manufatured on demand supplier.

Table of Contents

1. Basics.- 2. Independent Component Analysis.- 3. A Unifying Information-Theoretic Framework for ICA.- 4. Blind Separation of Time-Delayed and Convolved Sources.- 5. ICA Using Overcomplete Representations.- 6. First Steps towards Nonlinear ICA.- 7. Biomedical Applications of ICA.- 8. ICA for Feature Extraction.- 9. Unsupervised Classification with ICA Mixture Models.- 10. Conclusions and Future Research.- About the author.

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