Nickisch, H and Rasmussen, CE (2010) Gaussian mixture modeling with Gaussian process latent variable models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6376 L. pp. 272-282. ISSN 0302-9743Full text not available from this repository.
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||15 Dec 2015 12:43|
|Last Modified:||05 May 2016 00:55|