An improved geometric overcomplete blind source separation algorithm

Authors:
Fabian J. Theis;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;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

In this paper, we generalize the efficient geometric ICA algorithm FastGeo to overcomplete settings with more sources than sensors. The solution to this underdetermined problem will be presented in a two step approach. With geometric ICA we get an efficient method for the step--matrix-recovery--while the second step--source-recovery-- uses a maximum-likelihood approach.