A comparison of mixture models for density estimation

被引：0

作者：

Moerland, P ^{[1
]}

机构：

[1] IDIAP, Martigny, Switzerland

来源：

NINTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS (ICANN99), VOLS 1 AND 2 | 1999年 / 470期

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：25 / 30

页数：6

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