Hyvärinen / Karhunen / Oja Independent Component Analysis

1. Auflage 2004
ISBN: 978-0-471-46419-8
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, Band 1, 504 Seiten, E-Book

Reihe: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control

A comprehensive introduction to ICA for students andpractitioners
Independent Component Analysis (ICA) is one of the most excitingnew topics in fields such as neural networks, advanced statistics,and signal processing. This is the first book to provide acomprehensive introduction to this new technique complete with thefundamental mathematical background needed to understand andutilize it. It offers a general overview of the basics of ICA,important solutions and algorithms, and in-depth coverage of newapplications in image processing, telecommunications, audio signalprocessing, and more.
Independent Component Analysis is divided into four sections thatcover:
* 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 theircontributions to the development of ICA and here cover all therelevant theory, new algorithms, and applications in variousfields. Researchers, students, and practitioners from a variety ofdisciplines will find this accessible volume both helpful andinformative.

Autoren/Hrsg.

Hyvärinen, Aapo

Karhunen, Juha

Oja, Erkki

Inhaltsverzeichnis

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.

Über Autor(innen)

AAPO HYVÄRINEN, PhD, is Senior Fellow of the Academy ofFinland and works at the Neural Networks Research Center ofHelsinki University of Technology in Finland.
JUHA KARHUNEN and ERKKI OJA are professors at the Neural NetworksResearch Center of Helsinki University of Technology in Finland.