Estimating functions for blind separation when sources have variance dependencies

Motoaki Kawanabe, Klaus Robert Müller

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

A blind separation problem where the sources are not independent, but have variance dependencies is discussed. For this scenario Hyvärinen and Hurri (2004) proposed an algorithm which requires no assumption on distributions of sources and no parametric model of dependencies between components. In this paper, we extend the semiparametric approach of Amari and Cardoso (1997) to variance dependencies and study estimating functions for blind separation of such dependent sources. In particular, we show that many ICA algorithms are applicable to the variance-dependent model as well under mild conditions, although they should in principle not. Our results indicate that separation can be done based only on normalized sources which are adjusted to have stationary variances and is not affected by the dependent activity levels. We also study the asymptotic distribution of the quasi maximum likelihood method and the stability of the natural gradient learning in detail. Simulation results of artificial and realistic examples match well with our theoretical findings.

Original languageEnglish
JournalJournal of Machine Learning Research
Volume6
Publication statusPublished - 2005

Keywords

  • Blind source separation
  • Estimating functions
  • Independent component analysis
  • Semiparametric statistical models
  • Variance dependencies

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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