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Unsupervised Adaptive Filtering
Volume 1: Blind Source Separation
Volume 2: Blind Convolution
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Edited by Simon Haykin, McMaster University, Ontario, Canada
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0471 29412 8
April 2000
Hardback
Vol. 1
446pp
0471 37941 7
April 2000
Hardback
Vol. 2
200pp
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Unsupervised Adaptive Filtering is an area of intense research, with applications found in
signal processing, information theory, imaging and remote sensing among others. Unsupervised refers to the
automatic response of the system to changing conditions, enabling these filters to adapt to different situations
without human guidance. Presenting the foremost research in this area, these volumes provide a definitive source
of learning about the current state-of-the-art in the field.
Contents:
Vol 1:
- Natural Gradient Adaptation
- Blind Signal Separation and Extraction - Neural and Information-Theoretic Approaches
- Entropic Contrasts for Source Separation: Geometry and Stability
- Blind Source Separation: Models, Concepts, Algorithms and Performance
- Information Theory, Independent Component Analysis and Applications
- Information-Theoretic Learning
- Blind Separation of Delayed and Convolved Sources
- Blind Deconvolution of Multipath Mixtures
- The Core of FSE-CMA Behaviour Theory
- Relationships Between Blind Deconvolution and Blind Source Separation
- Blind Source Separation Based on Multiuser Kurtosis Optimization Criteria.
Vol 2:
- The Core of FSE-CMA Behaviour Theory
- Relationships between Blind Deconvolution and Blind Source Separation
- Blind Separation of Independent Sources Based on Multiuser Kurtosis Optimization Criteria.
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