Comparison of mixture models for density estimation

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Abstract

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.

Original languageEnglish
Title of host publicationIEE Conference Publication
PublisherIEE
Pages25-30
Number of pages6
Edition470
ISBN (Print)0852967217
Publication statusPublished - 1999
EventProceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)' - Edinburgh, UK
Duration: 7 Sept 199910 Sept 1999

Publication series

NameIEE Conference Publication
Number470
Volume1

Conference

ConferenceProceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)'
CityEdinburgh, UK
Period7/09/199910/09/1999

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