A new iterative initialization of EM algorithm for Gaussian mixture models

The expectation maximization (EM) algorithm is a common tool for estimating the parameters of Gaussian mixture models (GMM). However, it is highly sensitive to initial value and easily gets trapped in a local optimum. To address these problems, a new iterative method of EM initialization (MRIPEM) is...

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Veröffentlicht in:	PloS one Jg. 18; H. 4; S. e0284114
Hauptverfasser:	You, Jie, Li, Zhaoxuan, Du, Junli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 13.04.2023 Public Library of Science (PLoS)
Schlagworte:	Algorithms Analysis Cluster Analysis Clustering Computer and Information Sciences Covariance matrix Data mining Eigenvectors Evaluation Gaussian processes Iterative methods Learning Medicine and Health Sciences Methods Mixtures Normal Distribution Optimization Parameter estimation Parameter sensitivity People and Places Physical Sciences Probabilistic models Probability Probability distribution Random variables Research and Analysis Methods Statistical analysis Stochasticity China
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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