Skip to Main content Skip to Navigation
Conference papers

Self-organization by optimizing free-energy

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [8 references]  Display  Hide  Download
Contributor : Jakob Verbeek Connect in order to contact the contributor
Submitted on : Tuesday, March 8, 2011 - 3:02:27 PM
Last modification on : Monday, September 25, 2017 - 10:08:04 AM
Long-term archiving on: : Thursday, June 9, 2011 - 2:44:44 AM


Files produced by the author(s)


  • 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⟩



Les métriques sont temporairement indisponibles