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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Bibliographic Details
Published in:	IEEE journal of selected topics in signal processing Vol. 4; no. 3; pp. 548 - 559
Main Authors:	Markley, Scott C, Miller, David J
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.06.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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