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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE journal of selected topics in signal processing Jg. 4; H. 3; S. 548 - 559
Hauptverfasser:	Markley, Scott C, Miller, David J
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2010 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!