Unsupervised Learning of Finite Mixture Models

DMCA

Unsupervised Learning of Finite Mixture Models (2000)

Download From

IEEE

Download Links

Other Repositories/Bibliography

DBLP

by Mário A. T. Figueiredo , Anil K. Jain

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	418 - 22 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}
}

OpenURL

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

Keyphrases

finite mixture model parameter space preestimated candidate model new unsupervised algorithm convergence towards adjective unsupervised careful initialization mixture fitting wellknown drawback multivariate data standard expectation-maximization model selection criterion singular estimate

DMCA