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一類基于多變量密度估計的盲源分離方法

何振亞,  楊綠溪,  劉琚,  魯子奕,  何晨

何振亞, 楊綠溪, 劉琚, 魯子奕, 何晨. 一類基于多變量密度估計的盲源分離方法[J]. 電子與信息學報, 2001, 23(4): 345-353.
引用本文: 何振亞, 楊綠溪, 劉琚, 魯子奕, 何晨. 一類基于多變量密度估計的盲源分離方法[J]. 電子與信息學報, 2001, 23(4): 345-353.
He Zhenya, Yang Luxi, Liu Ju, Lu Ziyi, He Chen. A CLASS OF APPROACHES FOR BLIND SOURCE SEPARATION BASED ON MULTIVARIATE DENSITY ESTIMATION[J]. Journal of Electronics & Information Technology, 2001, 23(4): 345-353.
Citation: He Zhenya, Yang Luxi, Liu Ju, Lu Ziyi, He Chen. A CLASS OF APPROACHES FOR BLIND SOURCE SEPARATION BASED ON MULTIVARIATE DENSITY ESTIMATION[J]. Journal of Electronics & Information Technology, 2001, 23(4): 345-353.

一類基于多變量密度估計的盲源分離方法

A CLASS OF APPROACHES FOR BLIND SOURCE SEPARATION BASED ON MULTIVARIATE DENSITY ESTIMATION

  • 摘要: 該文給出了一類獨立源信號盲分離的訓練算法。該類算法都以測度概率密度函數(shù)的Kullback-Leibler距離作為目標函數(shù),用來衡量源信號各分量的獨立性。該文利用多變量概率密度估計技術和自然梯度優(yōu)化算法,使目標函數(shù)最小化,得出了兩種分離算法。計算機仿真結果表明了算法的有效性。并與Infomax算法比較,性能較優(yōu)。
    Abstract: A class of learning algorithms is drived for blind separation of independent source signals in this paper. These algorithms are based on minimizing a contrast function defined in terms of the Kullback-Leibler distance. By utilizing the technique of multivariate density esti-mation, two types of separating algorithms are obtained. Simulations illustrate the effectiveness of the algorithms.
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  • C. Jutten, J. Herault, Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic structure, Signal Processing, 1991, 24(1), 1-10.[2]P. Comon, Independent component analysis, A new concept? Signal Processing, 1994, 36(3), 287-314.[3]劉琚,魯子奕,何振亞等,基于信息理論準則的盲源分離方法,應用科學學報,1999,17(2),156-162.[4]H.H. Yang, S. Amari, Adaptive online learning algorithms for blind separation: Maximum entropy and minimum mutual information, Neural Computation, 1997, 9(7), 1457-1482.[5]A.J. Bell, T. J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 1995, 7(6), 1129-1159.[6]D. Obradovic, G. Deco, Information maximization and independent component analysis, is there a difference? Neural Computation, 1998, 10(8), 2085-2101.[7]J.F. Cardoso, B. Laheld, Equivariant adaptive source separation, IEEE Trans. on Signal Processing, 1996, 44(12), 3017-3030.[8]J.N. Hwang, S. R. Lay, A. Lippman, Nonparametric multivariate density estimation, A comparative study. IEEE Trans. on Signal Processing, 1994, 42(10), 2795-2810.[9]J.N. Hwang, S. R. Lay, A. Lippman, Unsupervised learning for multivariate probability density estimation, Radial basis and projection pursuit, IEEE Int. Conf. Neural Networks, 1993, San Francisco, CA, 1486-1491.[10]B.A. Linde, R. M. Gray, An algorithm for vector quantizer design, IEEE Trans. on Commun,1980, 28(1), 84-95.
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出版歷程
  • 收稿日期:  1999-04-14
  • 修回日期:  1999-10-13
  • 刊出日期:  2001-04-19

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