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 loglikelihood 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. Keywords: self-organizing map, mixture modeling, variational EM.
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