论文信息 - A New Information-Theoretic Based Ica Algorithm For Blind Signal Separation

A New Information-Theoretic Based Ica Algorithm For Blind Signal Separation

Abstract The typical information-theoretic approaches such as INFO MAX and MMI perform independent component analysis (ICA) by using a fixed nonlinearity function. Consequently, they can only separate either sub-Gaussian or super-Gaussian source signals, but not both. This article considers a flexible nonlinearity function that is a single polynomial term with the exponent learnable. The separation ability of this function is analysed, and a new ICA algorithm is proposed. The experiments have shown that this algorithm can successfully separate the mixture of sub-Gaussian and super-Gaussian sources.

Y.-M. Cheung | L. Xu

[1] Andrzej Cichocki,et al. Stability Analysis of Learning Algorithms for Blind Source Separation , 1997, Neural Networks.

[2] Andrzej Cichocki,et al. New learning algorithm for blind separation of sources , 1992 .

[3] Barak A. Pearlmutter,et al. A Context-Sensitive Generalization of ICA , 1996 .

[4] Shun-ichi Amari,et al. Stability Analysis Of Adaptive Blind Source Separation , 1997 .

[5] J. Nadal,et al. Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer Network 5 , 1994 .

[6] Shun-ichi Amari,et al. Independent component analysis by the information-theoretic approach with mixture of densities , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[7] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[8] Shun-ichi Amari,et al. Learned parametric mixture based ICA algorithm , 1998, Neurocomputing.