title: An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation creator: Sprekeler, Dr. Henning creator: Zito, Tiziano creator: Wiskott, Dr. Laurenz subject: Machine Learning description: 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. date: 2010-10 type: Preprint type: NonPeerReviewed format: application/pdf identifier: http://cogprints.org/7056/1/SprekelerZitoWiskott-Cogprints-2010.pdf identifier: Sprekeler, Dr. Henning and Zito, Tiziano and Wiskott, Dr. Laurenz (2010) An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation. [Preprint] relation: http://cogprints.org/7056/