## AbstractMixture probability densities are popular models that are used in several data mining and machine learning applications, e.g., clustering. A standard algorithm for learning such models from data is the Expectation-Maximization (EM) algorithm. However, EM can be slow with large datasets, and therefore approximation techniques are needed. In this paper we propose a variational approximation to the greedy EM algorithm which offers speedups that are at least linear in the number of data points. Moreover, by strictly increasing a lower bound on the data log-likelihood in every learning step, our algorithm guarantees convergence. We demonstrate the proposed algorithm on a synthetic experiment where satisfactory results are obtained.
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