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ICPR
2000
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Unsupervised Selection and Estimation of Finite Mixture Models

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Unsupervised Selection and Estimation of Finite Mixture Models
We propose a new method for fitting mixture models that performs component selection and does not require external initialization. The novelty of our approach includes: a minimum message length (MML) type model selection criterion; the inclusion of the criterion into the expectation-maximization (EM) algorithm (which also increases its ability to escape from local maxima); an initialization strategy supported on the interpretation of EM as a self-annealing algorithm.
Anil K. Jain, Mário A. T. Figueiredo
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2000
Where ICPR
Authors Anil K. Jain, Mário A. T. Figueiredo
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