Independent Component Analysis
Aapo Hyvärinen, Juha Karhunen, Erkki Oja
Publisher: Hoboken : John Wiley & Sons, Inc., 2004.
ISBN: 0471464198
DDC: 519.535
LCC: QA278
Edition: (electronic bk.)135.5 (NL)
Summary:
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.
Notes:
Contents; Preface; 1 Introduction; 2 Random Vectors and Independence;
3 Gradients and Optimization Methods; 4 Estimation Theory; 5
Information Theory; 6 Principal Component Analysis and Whitening; 7
What is Independent Component Analysis?; 8 ICA by Maximization of
Nongaussianity; 9 ICA by Maximum Likelihood Estimation; 10 ICA by
Minimization of Mutual Information; 11 ICA by Tensorial Methods; 12
ICA by Nonlinear Decorrelation and Nonlinear PCA; 13 Practical
Considerations; 14 Overview and Comparison of Basic ICA Methods; 15
Noisy ICA; 16 ICA with Overcomplete Bases; 17 Nonlinear ICA.
19 Convolutive Mixtures and Blind Deconvolution; 20 Other Extensions;
21 Feature Extraction by ICA; 22 Brain Imaging Applications; 23
Telecommunications; 24 Other Applications; References; Index.
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