Hybrid linear and nonlinear complexity pursuit for blind source separation

Authors:
Zhenwei Shi;Hongjuan Zhang;Zhiguo Jiang
Affiliations:
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, PR China;Department of Mathematics, Shanghai University, Shanghai 200444, PR China;Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, PR China
Venue:
Journal of Computational and Applied Mathematics
Year:
2012

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Abstract

Blind source separation (BSS) is an increasingly popular data analysis technique with many applications. Several methods for BSS using the statistical properties of original sources have been proposed; for a famous case, non-Gaussianity, this leads to independent component analysis (ICA). In this paper, we propose a hybrid BSS method based on linear and nonlinear complexity pursuit, which combines three statistical properties of source signals: non-Gaussianity, linear predictability and nonlinear predictability. A gradient learning algorithm is presented by minimizing a loss function. Simulations verify the efficient implementation of the proposed method.