We introduce a novel way of performing independent component analysis using
a constrained version of the expectation-maximization (EM) algorithm. The
source distributions are modeled as D one-dimensional mixtures of gaussians
. The observed data are modeled as linear mixtures of the sources with addi
tive, isotropic noise. This generative model is fit to the data using const
rained EM. The simpler "soft-switching" approach is introduced, which uses
only one parameter to decide on the sub- or supergaussian nature of the sou
rces. We explain how our approach relates to independent factor analysis.