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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE journal of selected topics in signal processing Ročník 4; číslo 3; s. 548 - 559
Hlavní autori:	Markley, Scott C, Miller, David J
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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 prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!