Fast Algorithms for Bayesian Independent Component Analysis

Submitted for ICA 2000.

Harri Lappalainen and Petteri Pajunen

Helsinki University of Technology, Neural Networks Research Centre
P.O.Box 5400, FIN-02015 HUT, Espoo, Finland
E-mail: Harri.Lappalainen@hut.fi, Petteri.Pajunen@hut.fi
URL: http://www.cis.hut.fi/

Abstract:

Fast algorithms for linear blind source separation are developed. The fast convergence is first derived from low-noise approximation of the EM-algorithm given in [2], to which a modification is made that leads as a special case to the FastICA algorithm [5]. The modification is given a general interpretation and is applied to Bayesian blind source separation of noisy signals.