Estimating Functions for Blind Separation when Sources Have Variance-Dependencies

The blind separation problem where the sources are not independent, but have variance-dependencies is discussed. Hyv¨arinen and Hurri[1] 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 statistical approach of Amari and Cardoso[2] under variancedependencies and study estimating functions for blind separation of such dependent sources. In particular, we show that many of ICA algorithms are applicable to the variance-dependent model as well. Our theoretical consequences were confirmed by artificial and realistic examples.

• Type: Conference
• Year: 2004
• Where: ICA
• Authors: Motoaki Kawanabe, Klaus-Robert Müller
• Keywords: Blind Separation, Blind Separation Problem, ICA 2004, Semiparametric Statistical Approach