Natural gradient learning for second-order nonstationary source separation

Seungjin Choi, Andrzej Cichocki, Shunichi Amari

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

In this paper we consider a problem of source separation when sources are second-order non-stationary stochastic processes. We employ the natural gradient method and develop learning algorithms for both linear feedback and feedforward neural networks. Thus our algorithms possess equivariant property. Local stability analysis shows that separating solutions are always locally stable stationary points of the proposed algorithms, regardless of probability distributions of sources.

Original languageEnglish
Pages654-658
Number of pages5
Publication statusPublished - 2002
Externally publishedYes
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: 12 May 200217 May 2002

Conference

Conference2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/TerritoryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

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