Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation

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Springer-Verlag New York Incorporated, 1999 - Computers - 271 pages
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This volume presents the theory and applications of self-organising neural network models which perform the Independent Component Analysis (ICA) transformation and Blind Source Separation (BSS). It is largely self-contained, covering the fundamental concepts of information theory, higher order statistics and information geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail. There is also in-depth coverage of the following application areas; noise reduction, speech enhancement in noisy environments, image enhancement, feature extraction for classification, data analysis and visualisation, data mining and biomedical data analysis. Self-Organising Neural Networks will be of interest to postgraduate students and researchers in Connectionist AI, Signal Processing and Neural Networks, research and development workers, and technology development engineers and research engineers.

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Contents

Background to Blind Source Separation
5
Fourth Order Cumulant Based Blind Source Separation
35
SelfOrganising Neural Networks
47
Copyright

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About the author (1999)

Mark Girolami is Professor of Computing and Inferential Science in the Department of Computing Science and the Department of Statistics at the University of Glasgow.

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