Independent Component Analysis (Adaptive and Learning Systems for Signal Processing, Communications and control Series)

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Independent Component Analysis (Adaptive and Learning Systems for Signal Processing, Communications and control Series)

Hyvarinen, Aapo/ Karhunen, Juha/ Oja, Erkki

ウェブストア価格 ¥44,234（本体¥40,213）
Wiley-Interscience（2001/05発売）
外貨定価 US$ 209.95
ポイント 402pt

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製本 Hardcover:ハードカバー版／ページ数 481 p.
言語 ENG
商品コード 9780471405405
DDC分類 519.535

基本説明

ICA is a statistical technique for revealing hidden factors from multiple measurements.

Full 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.

Preface.

Introduction.

MATHEMATICAL PRELIMINARIES.

Random Vectors and Independence.

Gradients and Optimization Methods.

Estimation Theory.

Information Theory.

Principal Component Analysis and Whitening.

BASIC INDEPENDENT COMPONENT ANALYSIS.

What is Independent Component Analysis?

ICA by Maximization of Nongaussianity.

ICA by Maximum Likelihood Estimation.

ICA by Minimization of Mutual Information.

ICA by Tensorial Methods.

ICA by Nonlinear Decorrelation and Nonlinear PCA.

Practical Considerations.

Overview and Comparison of Basic ICA Methods.

EXTENSIONS AND RELATED METHODS.

Noisy ICA.

ICA with Overcomplete Bases.

Nonlinear ICA.

Methods using Time Structure.

Convolutive Mixtures and Blind Deconvolution.

Other Extensions.

APPLICATIONS OF ICA.

Feature Extraction by ICA.

Brain Imaging Applications.

Telecommunications.

Other Applications.

References.

Index.