Unsupervised Learning of Finite Mixture Models

Authors:
Mario A. T. Figueiredo;Anil K. Jain
Affiliations:
Instituto Superior Técnico, Lisboa, Portugal;Michigan State Univ., East Lansing
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2002

Citing 22
Cited 313

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Abstract

This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective 驴unsupervised驴 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.