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.
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Theis, F.J., Puntonet, C.G., Lang, E.W. (2003). An improved geometric overcomplete blind source separation algorithm. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_34
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DOI: https://doi.org/10.1007/3-540-44869-1_34
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