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Conference Papers Year : 2003

Self-organization by optimizing free-energy

Jakob Verbeek
Nikos Vlassis
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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.
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Dates and versions

inria-00321491 , version 1 (02-02-2011)
inria-00321491 , version 2 (08-03-2011)


  • HAL Id : inria-00321491 , version 2


Jakob Verbeek, Nikos Vlassis, Ben Krose. Self-organization by optimizing free-energy. 11th European Symposium on Artificial Neural Networks (ESANN '03), Apr 2003, Bruges, Belgium. pp.125-130. ⟨inria-00321491v2⟩
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