Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
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.
| Author(s): | Nickisch, H. and Rasmussen, CE. |
| Year: | 2010 |
| Month: | June |
| Day: | 0 |
| Bibtex Type: | Technical Report (techreport) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Max Planck Institute for Biological Cybernetics |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@techreport{6634,
title = {Gaussian Mixture Modeling with Gaussian Process Latent Variable Models},
abstract = {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.},
organization = {Max-Planck-Gesellschaft},
institution = {Max Planck Institute for Biological Cybernetics},
school = {Biologische Kybernetik},
month = jun,
year = {2010},
slug = {6634},
author = {Nickisch, H. and Rasmussen, CE.},
month_numeric = {6}
}