An Extension of Slow Feature Analysis for Nonlinear Blind Source Separation

Henning Sprekeler, Tiziano Zito, Laurenz Wiskott; 15(Mar):921−947, 2014.

Abstract

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 high reliability. 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.

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