Overview
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (10 chapters)
-
Front Matter
-
Independent Component Analysis: Theory
-
Front Matter
-
-
Independent Component Analysis: Applications
-
Front Matter
-
-
Back Matter
Keywords
About this book
This book presents theories and applications of ICA and includes invaluable examples of several real-world applications. Based on theories in probabilistic models, information theory and artificial neural networks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, nonlinear PCA, Bussgang algorithm and cumulant-based methods are reviewed and put in an information theoretic framework to unify several lines of ICA research. An algorithm is presented that is able to blindly separate mixed signals with sub- and super-Gaussian source distributions. The learning algorithms can be extended to filter systems, which allows the separation of voices recorded in a real environment (cocktail party problem).
The ICA algorithm has been successfully applied to many biomedical signal-processing problems such as the analysis of electroencephalographic data and functional magnetic resonance imaging data. ICA applied to images results in independent image components that can be used as features in pattern classification problems such as visual lip-reading and face recognition systems. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification.
Independent Component Analysis: Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation. It is essential reading for researchers and practitioners with an interest in ICA.
Authors and Affiliations
Bibliographic Information
Book Title: Independent Component Analysis
Book Subtitle: Theory and Applications
Authors: Te-Won Lee
DOI: https://doi.org/10.1007/978-1-4757-2851-4
Publisher: Springer New York, NY
-
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag US 1998
Hardcover ISBN: 978-0-7923-8261-4Published: 31 October 1998
Softcover ISBN: 978-1-4419-5056-7Published: 03 December 2010
eBook ISBN: 978-1-4757-2851-4Published: 17 April 2013
Edition Number: 1
Number of Pages: XXXIV, 210
Topics: Artificial Intelligence, Signal, Image and Speech Processing, Complex Systems, Statistical Physics and Dynamical Systems