"7056","An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation","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.","http://cogprints.org/7056/","Sprekeler, Dr. Henning and Zito, Tiziano and Wiskott, Dr. Laurenz","UNSPECIFIED","       <span class=\"person_name\">Sprekeler, Dr. Henning</span> and <span class=\"person_name\">Zito, Tiziano</span> and <span class=\"person_name\">Wiskott, Dr. Laurenz</span>     (2010)  <a href=\"http://cogprints.org/7056/\"><em>An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation.</em></a>       [Preprint]           ","henning.sprekeler@epfl.ch,tiziano.zito@bccn-berlin.de,l.wiskott@biologie.hu-berlin.de","2010-10"