Independent Component Analysis - Theory and Applications (Hardcover)

by Te-Won Lee (Author)

Editorial Reviews

Product Description
Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, 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 invaluable 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. Independent Component Analysis: Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation. It is essential reading for researchers and practitioners with an interest in ICA.

Product Details

• Hardcover: 248 pages
• Publisher: Springer; 1st edition (October 31, 1998)
• Language: English
• ISBN-10: 0792382617
• ISBN-13: 978-0792382614
• Product Dimensions: 9.3 x 6.7 x 0.8 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 2.8 out of 5 stars See all reviews (4 customer reviews)
• Amazon.com Sales Rank: #1,973,239 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

6 of 6 people found the following review helpful:

2.0 out of 5 stars Great Expectations !!, July 18, 2001

By	khaled (Jordan) - See all my reviews

This book is not what you expect at all because :

(1) It is merely a collection of papers for the same author lacking proper organization;

(2) Blank pages are deliberately left between chapters, also figures are placed on separate pages this can tell you a lot regarding the quality you expect;

(3) The book is expensive ,I think the author should make it the half or less ;

(4) A researcher in this field hardly sees the book as a reference;

(5) Other topics are ignored ex:Tensoral methods;ICA is not only about Infomax;

(6) The first pages compose the climax of the book ,the rest is just loose and even abscent concepts;

(7) Finally,I think that the book was published too early ,it seems a lot of maturity could have been witnessed if the author waited instead.

Anyone new will be presented to the name of the subject but not the subject itself .The book by Hyvarinen should be available ... ,go for it.I believe life will be a lot easier .The latter is divided into four parts which clearly puts the reader in the right place to start and are : (I) Mathematical Preliminaries (II) Basic Independent Component Analysis (III) Extensions and related Methods (IV) Applications of ICA ,also after reading the sample chapter and contents I think you will not be disappointed . ....

3 of 3 people found the following review helpful:

4.0 out of 5 stars What a price ?!, April 27, 2002

By	khaled (Jordan) - See all my reviews

The first part of the book is the best part, it deals with ICA in the information theoretic framework and shows how the (ML) and (infomax) are closely related. However, it is not for beginners since the background material is abbreviated as well as the mathematical exposition of this book assumes the preknowledge in ICA theory.The overall impression one gets is that the book is too short, knowing that the book is more or less a collection of the author's papers, this should not be surprising at all.

I rate it 4/5 because of its expensive price.

2 of 3 people found the following review helpful:

3.0 out of 5 stars Very good for a first book in the field, October 13, 2000

By	"mucegai" (Syracuse, NY) - See all my reviews

Presents clearly the problems and the method. The introductory part was helpful in understanding the ICA theoretical model although more detail on kurtosis description would have been beneficial. I liked the infomax algorithm and the way it was presented. On the down side: some minor erroneus explanations found. I have the feeling that ICA is more than just infomax approach and that the title "Independent Component Analysis - an Infomax Approach" would have been more appropiate. On the application section, very good presentation of the signal separation but very succint explanation on natural images for example. Being the first book I see in the field, I think, a thorough presentation would have been helpful.