Independent Component Analysis

✍ Scribed by Aapo Hyvarinen, Juha Karhunen, Erkki Oja

• Publisher: Wiley-Interscience
• Year: 2001
• Tongue: English
• Leaves: 505
• Edition: 1
• Category: Library

✦ Synopsis

A comprehensive introduction to ICA for students and practitionersIndependent 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 modelsAuthors 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.

✦ Table of Contents

Independent Component Analysis......Page 1
Copyright......Page 5
Contents......Page 6
Preface......Page 18
Ch1 Introduction......Page 24
Part1 Mathematical Preliminaries......Page 36
Ch2 Random Vectors & Independence......Page 38
Ch3 Gradients & Optimization Methods......Page 80
Ch4 Estimation Theory......Page 100
Ch5 Information Theory......Page 128
Ch6 Principal Component Analysis & Whitening......Page 148
Part2 Basic Independent Component Analysis......Page 168
Ch7 What is Independent Component Analysis?......Page 170
Ch8 ICA by Maximization of Nongaussianity......Page 188
Ch9 ICA by Maximum Likelihood Estimation......Page 226
Ch10 ICA by Minimization of Mutual Information......Page 244
Ch11 ICA by Tensorial Methods......Page 252
Ch12 ICA by Nonlinear Decorrelation & Nonlinear PCA......Page 262
Ch13 Practical Considerations......Page 286
Ch14 Overview & Comparison of Basic ICA Methods......Page 296
Part3 Extensions & Related Methods......Page 314
Ch15 Noisy ICA......Page 316
Ch16 ICA with Overcomplete Bases......Page 328
Ch17 Nonlinear ICA......Page 338
Ch18 Methods using Time Structure......Page 364
Ch19 Convolutive Mixtures & Blind Deconvolution......Page 378
Ch20 Other Extensions......Page 394
Part4 Applications of ICA......Page 412
Ch21 Feature Extraction by ICA......Page 414
Ch22 Brain Imaging Applications......Page 430
Ch23 Telecommunications......Page 440
Ch24 Other Applications......Page 464
References......Page 472
Index......Page 499
Backcover......Page 505