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A Spurious Equilibria-free Learning Algorithm for the Blind Separation of Non-zoer Skewness Signals

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Abstract

We present a new learning algorithm for the blind separation of independent source signals having non-zero skewness (the 3rd-order cumulant) (the source signals have non-symmetric probability distribution.), from their linear mixtures. It is shown that for a class of source signals whose probability distribution functions is not symmetric, a simple adaptive learning algorithm using quadratic function (f(x)=x2) is very efficient for blind source separation task. It is proved that all stable equilibria of the proposed learning algorithm are desirable solutions. Extensive computer simulation experiments confirmed the validity of the proposed algorithm.

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Authors and Affiliations

  1. School of Electrical and Electronics Engineering, Chungbunk, National University, 48 Kaeshin-Dong, Cheongju, Chungbuk, 361–763, Korea

    Seungjin Choi

  2. Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA

    Ruey-Wen Liu

  3. Lab for Open Information Systems, Brain Science Institute, RIKEN, 2–1 Hirosawa, Wako-shi, Saitama, 351–1, Japan

    Andrzej Cichocki

Authors
  1. Seungjin Choi
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  2. Ruey-Wen Liu
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  3. Andrzej Cichocki
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Choi, S., Liu, RW. & Cichocki, A. A Spurious Equilibria-free Learning Algorithm for the Blind Separation of Non-zoer Skewness Signals. Neural Processing Letters 7, 61–68 (1998). https://doi.org/10.1023/A:1009688827236

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  • DOI: https://doi.org/10.1023/A:1009688827236

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