A comparison of mixture models for density estimation

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.


Published in:
Proceedings of the International Conference on Artificial Neural Networks (ICANN'99), 1, 25-30
Presented at:
Proceedings of the International Conference on Artificial Neural Networks (ICANN'99)
Year:
1999
Publisher:
London: IEE
Keywords:
Note:
(IDIAP-RR 98-14)
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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