An Adaptive Approach to Blind Source Separation Using a Self-Organzing Map and a Neural Gas

Authors:
Fabian J. Theis;Manuel R. Alvarez;Carlos G. Puntonet;Elmar W. Lang
Affiliations:
Institute of Biophysics, AG Neuro- and Bioinformatics, University of Regensburg, Regensburg, Germany D-93040;Dept. Arquitecura y Tecnologia de Computadores, Escuela Tcnica Superior de Ingenieria Informatica, Universidad de Granada, Granada E-18071;Dept. Arquitecura y Tecnologia de Computadores, Escuela Tcnica Superior de Ingenieria Informatica, Universidad de Granada, Granada E-18071;Institute of Biophysics, AG Neuro- and Bioinformatics, University of Regensburg, Regensburg, Germany D-93040
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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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.