Unervised Adaptive Filtering, Blind Source Separation: (Unervised Adaptive Filtering)

A complete, one-stop reference on the state of the act of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Topics in Volume I include:
* Neural and information-theoretic approaches to blind signal separation
* Models, concepts, algorithms, and performance of blind source separation
* Blind separation of delayed and convolved sources
* Blind deconvolution of multipath mixtures
* Applications of blind source separation
Volume II: Blind Deconvolution continues coverage with blind channel equalization and its relationship to blind source separation.

Table of Contents:
Introduction (S. Haykin).

Natural-Gradient Adaptation (S. Douglas & S. Amari).

Blind Signal Separation and Extraction: Neural and Information-Theoretic Approaches (S. Amari, et al.).

Entropic Contrasts for Source Separation: Geometry and Stability (J.-F. Cardoso).

Blind Source Separation: Models, Concepts, Algorithms and Performance (P. Comon & P. Chevalier).

Information Theory, Independent-Component Analysis, and Applications (A. Bell).

Information-Theoretic Learning (J. Principe, et al.).

Blind Separation of Delayed and Convolved Sources (K. Torkkola).

Blind Deconvolution of Multipath Mixtures (R. Lambert & C. Nikias).

Index.

About the Author :

SIMON HAYKIN, PhD, is University Professor and Director of the Adaptive Systems Laboratory at McMaster University.