Approximate Maximum Likelihood Source Separation Using the Natural Gradient

• CHOI Seungjin

  the Department of Computer Science and Engineering, POSTECH

• CICHOCKI Andrzej

  Brain-style Information Systems Research Group, BSI, RIKEN

• ZHANG Liqing

  Brain-style Information Systems Research Group, BSI, RIKEN

• AMARI Shunichi

  Brain-style Information Systems Research Group, BSI, RIKEN

抄録

This paper addresses a maximum likelihood method for source separation in the case of overdetermined mixtures corrupted by additive white Gaussian noise. We consider an approximate likelihood which is based on the Laplace approximation and develop a natural gradient adaptation algorithm to find a local maximum of the corresponding approximate likelihood. We present a detailed mathematical derivation of the algorithm using the Lie group invariance. Useful behavior of the algorithm is verified by numerical experiments.

収録刊行物

• IEICE transactions on fundamentals of electronics, communications and computer sciences

  IEICE transactions on fundamentals of electronics, communications and computer sciences 86 (1), 198-205, 2003-01-01

  一般社団法人電子情報通信学会

キーワード

• independent component analysis
• maximum likelihood estimation
• natural gradient
• source separation
• overdetermined mixtures

詳細情報

• CRID: 1573950402232043136
• NII論文ID: 110003212496
• NII書誌ID: AA10826239
• ISSN: 09168508
• 本文言語コード: en
• データソース種別:
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