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 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.
Choi
Seungjin
S
Department of Computer Science and Engineering, Pohang University of Science and Technology, Nam-gu, South Korea. seungjin@postech.ac.kr
Cichocki
Andrzej
A
Amari
Shunichi
S
D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer