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Blind Source Separation Using Quadratic form Innovation

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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.

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Authors and Affiliations

  1. Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, People’s Republic of China

    Zhenwei Shi, Xueyan Tan & Zhiguo Jiang

  2. Department of Mathematics, Shanghai University, Shanghai, 200444, People’s Republic of China

    Hongjuan Zhang

Authors
  1. Zhenwei Shi
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  2. Hongjuan Zhang
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  3. Xueyan Tan
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  4. Zhiguo Jiang
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Correspondence to Zhenwei Shi.

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Shi, Z., Zhang, H., Tan, X. et al. Blind Source Separation Using Quadratic form Innovation. Neural Process Lett 33, 83–97 (2011). https://doi.org/10.1007/s11063-010-9165-6

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  • DOI: https://doi.org/10.1007/s11063-010-9165-6

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