Unsupervised selection and estimation of finite mixture models

被引：0

作者：

Figueiredo, MAT ^{[1
]}

Jain, AK ^{[1
]}

机构：

[1] Univ Tecn Lisboa, Inst Telecomunicacoes, Inst Super Tecn, P-1049001 Lisbon, Portugal

来源：

15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS | 2000年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We describe a new method for fitting mixture models to multivariate data which performs component selection and does not require external initialization. The novelty of our approach includes: an MML-like (minimum message length) model selection criterion; inclusion of the criterion into the expectation-maximization (EM) algorithm (increasing its ability to escape from local maxima); an initialization strategy supported on the interpretation of EM as a self-annealing algorithm.

引用

页码：87 / 90

页数：4

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