A unifying criterion for instantaneous blind source separation based on correntropy

Authors:
Ruijiang Li;Weifeng Liu;Jose C. Principe
Affiliations:
Computational Neuro Engineering Laboratory, The University of Florida, P.O. Box 116130, Gainesville, FL 32611, USA;Computational Neuro Engineering Laboratory, The University of Florida, P.O. Box 116130, Gainesville, FL 32611, USA;Computational Neuro Engineering Laboratory, The University of Florida, P.O. Box 116130, Gainesville, FL 32611, USA
Venue:
Signal Processing
Year:
2007

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Abstract

Correntropy has recently been introduced as a generalized correlation function between two stochastic processes, which contains both high-order statistics and temporal structure of the stochastic processes in one functional form. Based on this blend of high-order statistics and temporal structure in a single functional form, we propose a unified criterion for instantaneous blind source separation (BSS). The criterion simultaneously exploits both spatial and spectral characteristics of the sources. Consequently, the new algorithm is able to separate independent, identically distributed (i.i.d.) sources, which requires high-order statistics; and it is also able to separate temporally correlated Gaussian sources with distinct spectra, which requires temporal information. Performance of the proposed method is compared with other popular BSS methods that solely depend on either high-order statistics (FastICA, JADE) or second-order statistics at different lags (SOBI). The new algorithm outperforms the conventional methods in the case of mixtures of sub-Gaussian and super-Gaussian sources.