Overcomplete ICA with a Geometric Algorithm

Authors:
Fabian J. Theis;Elmar Wolfgang Lang;Tobias Westenhuber;Carlos García Puntonet
Affiliations:
-;-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 4
Cited 0

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Abstract

We present an independent component analysis (ICA) algorithm based on geometric considerations [10] [11] to decompose a linear mixture of more sources than sensor signals. Bofill and Zibulevsky [2] recently proposed a two-step approach for the separation: first learn the mixing matrix, then recover the sources using a maximum-likelihood approach. We present an efficient method for the matrix-recovery step mimicking the standard geometric algorithm thus generalizing Bofill and Zibulevsky's method.