Approximate maximum likelihood source separation using the natural gradient

Seungjin Choi, Andrzej Cichocki, Liqing Zhang, Shun Ichi Amari

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)198-205
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number1
Publication statusPublished - Jan 2003
Externally publishedYes


  • Independent component analysis
  • Maximum likelihood estimation
  • Natural gradient
  • Overdetermined mixtures
  • Source separation


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