INDEPENDENT COMPONENT ANALYSIS IN THE PRESENCE OF GAUSSIAN NOISE BASED ON ESTIMATING FUNCTIONS
Motoaki Kawanabe
The problem of estimating the statistical model of in
dependent component analysis in the presence of Gaus
sian noise is considered. Because of the additive noise,
a combination of factor analysis and a noisefree ICA
algorithm doesn't give a consistent estimator of the
mixing matrix. In this paper, following the semipara
metric statistical approach to the noisefree ICA model
by Amari and Cardoso [1], we propose a method of
estimating the mixing matrix consistently even if the
additive noise exists. The proposed algorithm consists
of two stages: First find the factor subspace by means
of factor analysis, and then determine the directions of
independent components based on an estimating func
tion in this semiparametric model.