An adaptive approach to blind source separation using a self-organzing map and a neural gas

Fabian J. Theis, Manuel R. Álvarez, Carlos G. Puntonet, Elmar W. Lang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Blind source separation (BSS) tries to transform a mixed random vector in order to recover the original independent sources. We present a new approach to linear BSS by using either a self-organizing map (SOM) or a neural gas (NG). In comparison to other mixture-space analysis ('geometric') algorithms, these result in a considerable improvement in separation quality, although the computational cost is rather high. One goal of these algorithms is to establish connections between neural networks and BSS that could further be exploited by for example transferring convergence proofs for SOMs to geometric BSS algorithms.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJose Mira, Jose R. Alvarez
PublisherSpringer Verlag
Pages695-702
Number of pages8
ISBN (Print)354040211X, 9783540402114
DOIs
StatePublished - 2003
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2687
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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