Equivariant nonstationary source separation

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Equivariant nonstationary source separation
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
Seungjin Choi, Andrzej Cichocki, Shun-ichi Amari
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where NN
Authors Seungjin Choi, Andrzej Cichocki, Shun-ichi Amari
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