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Unsupervised Learning of Finite Mixture Models
March 2002 (vol. 24 no. 3)
pp. 381-396

This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective “unsupervised” is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion to choose one among a set of preestimated candidate models; instead, we seamlessly integrate estimation and model selection in a single algorithm. Our technique can be applied to any type of parametric mixture model for which it is possible to write an EM algorithm; in this paper, we illustrate it with experiments involving Gaussian mixtures. These experiments testify for the good performance of our approach.

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Index Terms:
finite mixtures, unsupervised learning, model selection, minimum message length criterion, Bayesian methods, expectation-maximization algorithm, clustering
Citation:
M.A.T. Figueiredo, A.K. Jain, "Unsupervised Learning of Finite Mixture Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 381-396, March 2002, doi:10.1109/34.990138
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