Independent Component Analysis

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John Wiley & Sons, Apr 5, 2004 - Science - 504 pages
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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.
 

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Contents

1 Introduction
1
MATHEMATICAL PRELIMINARIES
13
BASIC INDEPENDENT COMPONENT ANALYSIS
145
EXTENSIONS AND RELATED METHODS
291
APPLICATIONS OF ICA
389
References
449
Index
476
Copyright

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Page 472 - R. Hari, and E. Oja. Independent component analysis for identification of artifacts in Magnetoencephalographic recordings.
Page 458 - A family of fixed-point algorithms for independent component analysis. In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP '97), pages 3917-3920, Munich, Germany, 1997. 193. A. Hyvarinen. One-unit contrast functions for independent component analysis: A statistical analysis.
Page 458 - A. Hyvarinen. New approximations of differential entropy for independent component analysis and projection pursuit.
Page 451 - In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP '98), pages 3613-3616, Seattle, Washington, 1998.
Page 470 - EP Simoncelli and EH Adelson, "Noise removal via Bayesian wavelet coring," in Proc 3rd IEEE Int'l Conf on Image Proc, I, pp.
Page 470 - EP Simoncelli and O. Schwartz. Modeling surround suppression in VI neurons with a statistically-derived normalization model.
Page 454 - DL Donoho, IM Johnstone, G. Kerkyacharian, and D. Picard. Wavelet shrinkage: Asymptopia?

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About the author (2004)

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

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