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 language | English |
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Title of host publication | IEE Conference Publication |
Publisher | IEE |
Pages | 25-30 |
Number of pages | 6 |
Edition | 470 |
ISBN (Print) | 0852967217 |
Publication status | Published - 1999 |
Event | Proceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)' - Edinburgh, UK Duration: 7 Sept 1999 → 10 Sept 1999 |
Publication series
Name | IEE Conference Publication |
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Number | 470 |
Volume | 1 |
Conference
Conference | Proceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)' |
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City | Edinburgh, UK |
Period | 7/09/1999 → 10/09/1999 |