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一类基于多变量密度估计的盲源分离方法

何振亚 杨绿溪 刘琚 鲁子奕 何晨

何振亚, 杨绿溪, 刘琚, 鲁子奕, 何晨. 一类基于多变量密度估计的盲源分离方法[J]. 电子与信息学报, 2001, 23(4): 345-353.
引用本文: 何振亚, 杨绿溪, 刘琚, 鲁子奕, 何晨. 一类基于多变量密度估计的盲源分离方法[J]. 电子与信息学报, 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.

一类基于多变量密度估计的盲源分离方法

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

  • 摘要: 该文给出了一类独立源信号盲分离的训练算法。该类算法都以测度概率密度函数的Kullback-Leibler距离作为目标函数,用来衡量源信号各分量的独立性。该文利用多变量概率密度估计技术和自然梯度优化算法,使目标函数最小化,得出了两种分离算法。计算机仿真结果表明了算法的有效性。并与Infomax算法比较,性能较优。
  • 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]刘琚,鲁子奕,何振亚等,基于信息理论准则的盲源分离方法,应用科学学报,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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出版历程
  • 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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