http://cogprints.org/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.
Sprekeler, Dr. Henning
Zito, Tiziano
Wiskott, Dr. Laurenz
Machine Learning
Henning
Sprekeler
Tiziano
Zito
Laurenz
Wiskott