%A Dr. Henning Sprekeler
%A Tiziano Zito
%A Dr. Laurenz Wiskott
%T An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation
%X We present and test an extension of slow feature analysis as a novel approach to nonlinear blind source separation. The algorithm relies on temporal correlations and iteratively reconstructs a set of statistically independent sources from arbitrary nonlinear instantaneous mixtures. Simulations show that it is able to invert a complicated nonlinear mixture of two audio signals with a reliability of more than $90$\%. The algorithm is based on a  mathematical analysis of slow feature analysis for the case of input data that are generated from statistically independent sources.
%D 2010
%K slow feature analysis, nonlinear blind source separation, independent component analysis
%L cogprints7056

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