Blind Source Separation Using Quadratic form Innovation

Authors:
Zhenwei Shi;Hongjuan Zhang;Xueyan Tan;Zhiguo Jiang
Affiliations:
Image Processing Center, School of Astronautics, Beihang University, Beijing, People's Republic of China 100191;Department of Mathematics, Shanghai University, Shanghai, People's Republic of China 200444;Image Processing Center, School of Astronautics, Beihang University, Beijing, People's Republic of China 100191;Image Processing Center, School of Astronautics, Beihang University, Beijing, People's Republic of China 100191
Venue:
Neural Processing Letters
Year:
2011

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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 one, such as non-Gaussianity, which leads to independent component analysis (ICA). This paper proposes a blind source separation method based on a novel statistical property: the quadratic form innovation of original sources, which includes linear predictability and energy (square) predictability as special cases. A gradient learning algorithm is presented by minimizing a loss function of the quadratic form innovation. Also, we give the stability analysis of the proposed BSS algorithm. Simulations verify the efficient implementation of the proposed method.