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Unsupervised learning of finite mixture models

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2 Author(s)
M. A. T. Figueiredo ; Dept. of Electr. & Comput. Eng., Inst. of Telecommun., Lisbon, Portugal ; A. K. Jain

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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:24 ,  Issue: 3 )