Karvanen, J., Eriksson, J., Koivunen, V.
Maximum likelihood estimation of ICA-model for
wide class of source distributions
We propose two blind source separation techniques that are applicable
to a wide class of source distributions that may also be skewed and
may even have zero kurtosis. Skewed distributions are encountered in
many important application areas such as communications and biomedical
The methods are based on maximum likelihood approach where source
distributions are modeled adaptively by the Pearson system and the
Extended Generalized Lambda Distribution (EGLD). To compare the developed
methods with the existing methods, quantitative measures for the quality
of separation are used. Simulation experiments
demonstrate the good performance of proposed methods in the cases where
standard BSS methods perform poorly.