Comparison of mixture models for density estimation

Autor:	Perry Moerland
Přispěvatelé:	Epidemiology and Data Science, AII - Inflammatory diseases, APH - Methodology, APH - Personalized Medicine, AII - Infectious diseases
Jazyk:	angličtina
Rok vydání:	1999
Předmět:	Computer Science::Computer Vision and Pattern Recognition
Zdroj:	IEE Conference Publication, 25-30 ISSUE=470;STARTPAGE=25;ENDPAGE=30;TITLE=IEE Conference Publication Scopus-Elsevier
Popis:	Gaussian mixture models (GMMs) are a popular tool for density estimation. However, these models are limited by the fact that they either impose strong constraints on the covariance matrices of the component densities or no constraints at all. This paper presents an experimental comparison of GMMs and the recently introduced mixtures of linear latent variable models. It is shown that the latter models are a more flexible alternative for GMMs and often lead to improved results.
Databáze:	OpenAIRE
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