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Independent Component Analysis
 
 
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Independent Component Analysis [Hardcover]

Aapo Hyvärinen (Author), Juha Karhunen (Author), Erkki Oja (Author)
5.0 out of 5 stars  See all reviews (2 customer reviews)

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Book Description

047140540X 978-0471405405 May 18, 2001 1
A comprehensive introduction to ICA for students and practitioners
Independent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and more.
Independent Component Analysis is divided into four sections that cover:
* General mathematical concepts utilized in the book
* The basic ICA model and its solution
* Various extensions of the basic ICA model
* Real-world applications for ICA models
Authors Hyvarinen, Karhunen, and Oja are well known for their contributions to the development of ICA and here cover all the relevant theory, new algorithms, and applications in various fields. Researchers, students, and practitioners from a variety of disciplines will find this accessible volume both helpful and informative.

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Editorial Reviews

Review

"...researchers...introduce independent component analysis as a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals." (SciTech Book News, Vol. 25, No. 4, December 2001)

From the Back Cover

A comprehensive introduction to ICA for students and practitioners

Independent Component Analysis (ICA) is one of the most exciting new topics in fields such as neural networks, advanced statistics, and signal processing. This is the first book to provide a comprehensive introduction to this new technique complete with the fundamental mathematical background needed to understand and utilize it. It offers a general overview of the basics of ICA, important solutions and algorithms, and in-depth coverage of new applications in image processing, telecommunications, audio signal processing, and more.

Independent Component Analysis is divided into four sections that cover:
* General mathematical concepts utilized in the book
* The basic ICA model and its solution
* Various extensions of the basic ICA model
* Real-world applications for ICA models

Authors Hyv?rinen, Karhunen, and Oja are well known for their contributions to the development of ICA and here cover all the relevant theory, new algorithms, and applications in various fields. Researchers, students, and practitioners from a variety of disciplines will find this accessible volume both helpful and informative.

Product Details

  • Hardcover: 504 pages
  • Publisher: Wiley-Interscience; 1 edition (May 18, 2001)
  • Language: English
  • ISBN-10: 047140540X
  • ISBN-13: 978-0471405405
  • Product Dimensions: 9.5 x 6.1 x 1.1 inches
  • Shipping Weight: 1.9 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #662,345 in Books (See Top 100 in Books)

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23 of 25 people found the following review helpful:
5.0 out of 5 stars Comprehensive Book, October 29, 2001
By 
This review is from: Independent Component Analysis (Hardcover)
Independent Component Analysis is a young and interesting topic that gained attention and still receiving more of it.

Until now this is the best introduction that has been written.
It is comprehensive, clear and unbiased.

I think that the book is a step toward making the subject not only a common field of research but also a reference for those looking for new challenging topics.

What worths mentioning is that the authors are very envolved in the development of the theory of ICA ,other books are good but are deviated by their author's own approachs and this is normal but unhealthy for a first book on any field.

What constitutes a great help for understanding ICA are the relatively easy concepts if one just intend to pick an algorithm(ex:FastICA), but this is not the case regarding its theory.

One colleague once argued that ICA should have emerged long before the begining of the 90's, claiming that Gaussian forms
(Central Limit-Theorem) killed the idea of dealing with other kinds of distributions and therefore the signal processing community went assuming every thing was gaussian (noise was gaussian,signals are gaussian),but the emerge of HOS relaxed the gaussian restriction and ICA became possible and no longer 'blind' .

I think this should prepare researchers to deal with coming challengs more intelligently and efficiently .That is why I recommend this book since it tries to give a broad view to the subject .

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9 of 12 people found the following review helpful:
5.0 out of 5 stars Nice and detailed description of ICA, October 26, 2001
By A Customer
This review is from: Independent Component Analysis (Hardcover)
This is a nice and self-contained book on the subject of independent component analysis (ICA). The authors start with relevant mathematical and statistical background (in Part I) to prepare readers for the derivations of ICA (though seasoned researchers may want to skip the first part of this book). The authors discuss the motivation behind ICA and present several ways to derive ICA (since this subject has been approached by several communities). The authors also compare and discuss the pros and cons of these approaches. The authors discuss several applications using ICA in Part III.

Compared with other ICA books, this manuscript has much depth and completeness. I highly recommend this book to any reader interested in this topic.

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Inside This Book (learn more)
First Sentence:
"In this chapter, we review central concepts of probability theory, statistics, and random processes." Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nonpolynomial moment, independent subspace analysis, maximum nongaussianity, supergaussian densities, natural gradient algorithm, nongaussianity measures, nonlinear decorrelation, using negentropy, whitened mixtures, whitened data, maximizing nongaussianity, overcomplete bases, bias removal techniques, convolutive mixtures, sparse code shrinkage, cumulant tensor, mixing mapping, tanh nonlinearity, whitening matrix, natural gradient method, quadratic priors, using kurtosis, nonlinear blind source separation, independent component analysis, whiten the data
Key Phrases - Capitalized Phrases (CAPs): (learn more)
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