CUED Publications database

An extension of slow feature analysis for nonlinear blind source separation

Sprekeler, H and Zito, T and Wiskott, L (2014) An extension of slow feature analysis for nonlinear blind source separation. Journal of Machine Learning Research, 15. pp. 921-947. ISSN 1532-4435

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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. © 2014 Henning Sprekeler, Tiziano Zito and Laurenz Wiskott.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:06
Last Modified: 07 Sep 2017 01:46
DOI: