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The generative self-organizing map: a probabilistic generalization of Kohonen's SOM
Abstract : We present a variational Expectation-Maximization algorithm to learn proba- bilistic mixture models. The algorithm is similar to Kohonen's Self-Organizing Map algorithm and can be applied on any mixture model for which we can find a standard Expectation Maximization algorithm. We maximize the variational free- energy which sums data log-likelihood and Kullback-Leibler divergence between the neighborhood function and the posterior distribution on the components, given data. We illustrate the algorithm with an application on word clustering.
Cited literature [8 references]
https://hal.inria.fr/inria-00321505
Contributor : Jakob Verbeek <>
Submitted on : Wednesday, February 16, 2011 - 5:09:33 PM
Last modification on : Monday, September 25, 2017 - 10:08:04 AM
Document(s) archivé(s) le : Tuesday, May 17, 2011 - 2:34:55 AM
Contributor : Jakob Verbeek <>
Submitted on : Wednesday, February 16, 2011 - 5:09:33 PM
Last modification on : Monday, September 25, 2017 - 10:08:04 AM
Document(s) archivé(s) le : Tuesday, May 17, 2011 - 2:34:55 AM
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verbeek02tr.pdf
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