9780521792981

Independent Component Analysis: Principles and Practice

by
  • ISBN13:

    9780521792981

  • ISBN10:

    0521792983

  • Format: Hardcover
  • Copyright: 2001-03-26
  • Publisher: Cambridge University Press
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Summary

Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. 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, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field.

Table of Contents

Preface vii
Contributors xi
Introduction
1(70)
S. J. Roberts
R. M. Everson
Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity
71(24)
Aapo Hyvarinen
ICA, graphical models and variational methods
95(18)
H. Attias
Nonlinear ICA
113(22)
J. Karhunen
Separation of non-stationary natural signals
135(23)
Lucas C. Parra
Clay D. Spence
Separation of non-stationary sources: algorithms and performance
158(23)
Jean-Francois Cardoso
Dinh-Tuan Pham
Blind source separation by sparse decomposition in a signal dictionary
181(28)
M. Zihulevsky
B. A. Pearlmutter
P. Bofill
P. Kisilev
Ensemble Learning for blind source separation
209(25)
J. W. Miskin
D. J. C. MacKay
Image processing methods using ICA mixture models
234(20)
T.-W. Lee
M. S. Lewicki
Latent class and trait models for data classification and visualisation
254(26)
M. A. Girolami
Particle filters for non-stationary ICA
280(19)
R. M. Everson
S. J. Roberts
ICA: model order selection and dynamic source models
299(16)
W. D. Penny
S. J. Roberts
R. M. Everson
References 315(21)
Index 336

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