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. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Theis, F. J., Lang, E. W., Westenhuber, T., & Puntonet, C. G. (2002). Overcomplete ICA with a geometric algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 1049–1054). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_170
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