Independent Component Analysis

Book Description

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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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.

Customer Reviews:

Comprehensive Book.......2001-10-30

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 .

Nice and detailed description of ICA.......2001-10-27

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.

Book Description

Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions.

In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method.

An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls "the mathematical nuts and bolts" of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA.

Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code.

Customer Reviews:

Wonderful Book!.......2007-08-16

Eases the reader gradually through the foundations of ICA and treats various published methods in a contrasting manner. No other reference is needed while reading the book; he even gives the pronounciation of some of the greek letters in footnotes.

Stimulating introduction and review of ICA.......2007-07-03

This excellent book introduces the reader in the field of Independent Component Analysis providing the necessary fundamentals to understand and apply the different methods. The book also makes interesting links to other techniques. The author has succeeded at writing a very didactic text, not an easy task given the complexity of the matter, and at transmitting his enthusiasm to the reader.
I've enjoyed this book, which has been not only an introduction to ICA but which has brought me into ICA, stimulating my own experimentation with the technique.

Outstanding.......2006-11-27

Without this book I would never have understood the basics and finesses of ICA. Even if readers ar highly skilled in math reading this book will set out mile'stones' that will enhance the understanding of the ICA- problem, -tools and -possibilities.

Dr. G. Otte

The best introduction on the subject.......2006-05-05

I can't stress how reader friendly this book is. It is by far the best introduction on component analysis. It is written in such a way that those with a weaker math background can understand it while those with years of experience will not be bored, at certain times it even reads like a story.

It addition to being readable the book contains an impressive amount of content for its size. This content is presented in an organized manner, and in such a way that the user can immediately apply the techniques to their own problems.

If you are interested in independent component analysis or one of its relatives I highly recommend this valuable, reasonably price book.

James Stone's monograph: 'Independent Component Analysis'.......2006-01-10

James Stone's monograph is a refreshing new book amongst the many other `new books' on Independent Component Analysis (ICA). The author brings his teaching experience to present the theory and practice of ICA in a highly accessible form using a duplication of words and straight-forward mathematics.

Particular attention is given in the earlier chapters to the description of the linear signal mixing process giving the Reader a good basis for understanding the fundamental assumptions upon which ICA and its application to Blind Source Separation are based.

The book is aimed at the Reader with a technical but not necessarily formal mathematics background. Illustrative examples and functional algorithms in MatLab are frequent and references are made to the author's available electronic resources. As such it is suitable to both the newcomer to ICA, and to the more expert engineer or scientist.

This Reviewer rates this book very highly.

Independent Component Analysis: Principles and Practice

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Independent Component Analysis: Principles and Practice

Manufacturer: Cambridge University Press
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Binding: Hardcover

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Independent Component Analysis: A Tutorial Introduction (Bradford Books)

ASIN: 0521792983

Book Description

Independent Components Analysis (ICA) is an important tool for modeling and understanding empirical data sets. Belonging to the class of general linear models, it is a method of separating out independent sources from linearly mixed data. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading 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 applications in detail. Applications include biomedical examples, signal and 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 methods.

Advances in Independent Component Analysis (Perspectives in Neural Computing)

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Advances in Independent Component Analysis (Perspectives in Neural Computing)

Manufacturer: Springer
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Binding: Paperback

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ASIN: 1852332638

Book Description

Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.
It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.
Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

Algorithm Collections for Digital Signal Processing Applications using Matlab

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Algorithm Collections for Digital Signal Processing Applications using Matlab
E.S. Gopi
Manufacturer: Springer
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ASIN: 1402064098

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The Algorithms such as SVD, Eigen decomposition, Gaussian Mixture Model, HMM etc. are scattered in different fields. There is the need to collect all such algorithms for quick reference. Also there is the need to view such algorithms in application point of view. Algorithm Collections for Digital Signal Processing Applications using MATLAB attempts to satisfy the above requirement. Also the algorithms are made clear using MATLAB programs.

Blind Speech Separation (Signals and Communication Technology)

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Blind Speech Separation (Signals and Communication Technology)

Manufacturer: Springer
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ASIN: 1402064780

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This is the first book to provide a cutting edge reference to the fascinating topic of blind source separation (BSS) for convolved speech mixtures. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications. The individual chapters are designed to be tutorial in nature with specific emphasis on an in-depth treatment of state of the art techniques.

Blind Speech Separation is divided into three parts:

Part 1 presents overdetermined or critically determined BSS. Here the main technology is independent component analysis (ICA). ICA is a statistical method for extracting mutually independent sources from their mixtures. This approach utilizes spatial diversity to discriminate between desired and undesired components, i.e., it reduces the undesired components by forming a spatial null towards them. It is, in fact, a blind adaptive beamformer realized by unsupervised adaptive filtering.

Part 2 addresses underdetermined BSS, where there are fewer microphones than source signals. Here, the sparseness of speech sources is very useful; we can utilize time-frequency diversity, where sources are active in different regions of the time-frequency plane.

Part 3 presents monaural BSS where there is only one microphone. Here, we can separate a mixture by using the harmonicity and temporal structure of the sources. We can build a probabilistic framework by assuming a source model, and separate a mixture by maximizing the a posteriori probability of the sources.

Independent Component Analyses, Wavelets, and Neural Networks: 22-25 April, 2003, Orlando, Florida, USA (Proceedings of SPIE)

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Independent Component Analyses, Wavelets, and Neural Networks: 22-25 April, 2003, Orlando, Florida, USA (Proceedings of SPIE)

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Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, And Neural Networks III (Proceedings of SPIE)

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Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, And Neural Networks III (Proceedings of SPIE)
Harold H. Szu
Manufacturer: SPIE-International Society for Optical Engine
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Binding: Paperback

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ASIN: 0819458031

Independent Component Analysis - Theory and Applications

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What a price ?!
Great Expectations !!
An average book
Very good for a first book in the field

Independent Component Analysis - Theory and Applications
Te-Won Lee
Manufacturer: Springer
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Binding: Hardcover

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Independent Component Analysis: A Tutorial Introduction (Bradford Books)

ASIN: 0792382617

Book 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.

Customer Reviews:

What a price ?!.......2002-04-27

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.

Great Expectations !!.......2001-07-19

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 . ....

An average book.......2001-07-07

If the newcomer is expecting a textbook which gives a thorough and rigorous introduction to the subject, he will not find it here. Essentially, Independent Component Analysis by T. Lee is a compendium of Lee's work on the subject, being, for the most part, a regurgitation of his papers. This in itself is no cause for distress; however, I feel that perhaps some more detail and work could have gone into other researchers' avenues to the problem. For instance, cumulant based methods hardly make it into the book. The derivation of the most important formulas for multiple decorrelation algorithms are omitted. The Fixed-Point Method of Hyvarinen is omitted. A paragraph is given to algorithms which work entirely in the frequency domain. Short shrift is given to JADE.

The applications side is dominated nearly entirely by the biomedical applications to which Lee is associated with, with a small foray into the world of feature extraction.

The introduction and conclusions are well written, though more detail could have helped. There are a few errata throughout though this is normal for a first book. All in all a book with a rather narrow focus.

Very good for a first book in the field.......2000-10-13

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.

Independent Component Analysis and Blind Signal Separation: 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006, Proceedings (Lecture Notes in Computer Science)

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Independent Component Analysis and Blind Signal Separation: 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006, Proceedings (Lecture Notes in Computer Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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Independent Component Analysis: A Tutorial Introduction (Bradford Books)

ASIN: 3540326308

Book Description

This book constitutes the refereed proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation, ICA 2006, held in Charleston, SC, USA, in March 2006.

The 120 revised papers presented were carefully reviewed and selected from 183 submissions. The papers are organized in topical sections on algorithms and architectures, applications, medical applications, speech and signal processing, theory, and visual and sensory processing.

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