This site has been permanently archived. This is a static copy provided by the University of Southampton.

%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