## Unsupervised Learning of Finite Mixture Models (2000)

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Venue: | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |

Citations: | 267 - 20 self |

### BibTeX

@ARTICLE{Figueiredo00unsupervisedlearning,

author = {Mário A. T. Figueiredo and Anil K. Jain},

title = {Unsupervised Learning of Finite Mixture Models},

journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},

year = {2000},

volume = {24},

pages = {381--396}

}

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### Abstract

A new unsupervised algorithm for learning a finite mixture model from multivariate data is proposed. The adjective "unsupervised" is justified by two properties of the algorithm: (i) it is capable of selecting the number of components, and, (ii) unlike the standard expectation-maximization (EM) algorithm, it does not require careful initialization of the parameters. The proposed method also avoids another wellknown drawback of EM for mixture fitting: the possibility of convergence towards 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