Equivariant nonstationary source separation

Authors:
Seungjin Choi;Andrzej Cichocki;Shunichi Amari
Affiliations:
Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, South Korea;Brain-style Information Systems Research Group, Brain Science Institute, Riken, Japan;Brain-style Information Systems Research Group, Brain Science Institute, Riken, Japan
Venue:
Neural Networks
Year:
2002

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Abstract

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. [Neural Networks 8 (1995) 411] and derive natural gradient learning algorithms for both fully connected recurrent network and feedforward network. Since our algorithms employ the natural gradient method, they possess the equivariant property and find a steepest descent direction unlike the algorithm [Neural Networks 8 (1995) 411]. We also show that our algorithms are always locally stable, regardless of probability distributions of nonstationary sources.