Refereed full papers (journals, book chapters, international conferences)

2004

@article{VVK2004,
	vgclass =	{refpap},
	author =	{J. J. Verbeek and N. Vlassis and B. J. A. Kr\"{o}se},
	title =	{Self-organizing mixture models},
	journal =	{Neural Computation},
	year =	{2004},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.neucom.2004.04.008},
	abstract =	{We present an expectation-maximization (EM) algorithm that
	yields topology preserving maps of data based on probabilistic mixture
	models. Our approach is applicable to any mixture model for which we
	have a normal EM algorithm. Compared to other mixture model approaches
	to self-organizing maps (SOMs), the function our algorithm maximizes
	has a clear interpretation: it sums data log-likelihood and a penalty
	term that enforces self-organization. Our approach allows principled
	handling of missing data and learning of mixtures of SOMs. We present
	example applications illustrating our approach for continuous,
	discrete, and mixed discrete and continuous data.},
}