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Postnonlinear overcomplete blind source separation using sparse sources

Theis, Fabian J. ; Amari, S. Puntonet, Carlos G. , eds.



Abstract

We present an approach for blindly decomposing an observed random vector x into As where f is a diagonal function i.e. f=f_1 x ... x f_m with one-dimensional functions f_i and A an (m x n)-matrix. This postnonlinear model is allowed to be overcomplete, which means that less observations than sources (m$\lt$n) are given. In contrast to Independent Component Analysis (ICA) we do not assume the ...

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