Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures

Multivariate Gaussian mixture models (GMMs) are widely for density estimation, model-based data clustering, and statistical classification. A difficult problem is estimating the model order, i.e., the number of mixture components, and model structure. Use of full covariance matrices, with number of...

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Vydáno v:	IEEE journal of selected topics in signal processing Ročník 4; číslo 3; s. 548 - 559
Hlavní autoři:	Markley, Scott C, Miller, David J
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Algorithms Bayesian analysis Bayesian information criterion (BIC) Bayesian methods Classification Covariance matrix Criteria Density functional theory Expectation-maximization algorithms Gaussian generalized Expectation-maximization (GEM) algorithm Learning Mathematical models Measurement standards Minimization methods Multivariate Gaussian mixture model (GMM) Optimization methods Parameter estimation Robustness Studies Testing unsupervised learning
ISSN:	1932-4553, 1941-0484
On-line přístup:	Získat plný text
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