@article{ZivkHeij2004,
author="Z.Zivkovic and F.van der Heijden",
title="Recursive unsupervised learning of finite mixture models", 
journal="IEEE Transactions on Pattern Analysis and Machine Intelligence",
volume=26,
number=5,
year=2004,
abstract="There are two open problems when finite mixture densities are used
to model multivariate data: the selection of the number of
components and the initialization. In this paper we propose an
on-line (recursive) algorithm that estimates the parameters of the
mixture and that simultaneously selects the number of components.
The new algorithm starts with a large number of randomly
initialized components. A prior is used as a bias for maximally
structured models. A stochastic approximation recursive learning
algorithm is proposed to search for the maximum a posteriori (MAP)
solution and to discard the irrelevant components."
}