Self-Organization by Optimizing Free-Energy

Reference : Self-Organization by Optimizing Free-Energy


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/11060

Title :	Self-Organization by Optimizing Free-Energy
Language :	English
Author, co-author :	Verbeek, J. J. [> >]
	Vlassis, Nikos [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Kröse, B. J. A. [> >]
Publication date :	2003
Main document title :	Proc. of European Symposium on Artificial Neural Networks
Pages :	125-130
Peer reviewed :	Yes
Event name :	European Symposium on Artificial Neural Networks
Event date :	2003
Abstract :	[en] We present a variational Expectation-Maximization algorithm to learn probabilistic mixture models. The algorithm is similar to Kohonen's Self-Organizing Map algorithm and not limited to Gaussian mixtures. We maximize the variational free-energy that sums data log-likelihood and Kullback-Leibler divergence between a normalized neighborhood function and the posterior distribution on the components, given data. We illustrate the algorithm with an application on word clustering.
Permalink :	http://hdl.handle.net/10993/11060

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