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. Modeling of densities, aka unsupervised learning, is one of the central problems in machine learning. Despite its long history [1], density modeling remains a challenging task especially in hi...

• Type: Conference
• Year: 2010
• Where: DAGM
• Authors: Hannes Nickisch, Carl Edward Rasmussen
• Keywords: DAGM 2010, Density Estimates, Density Modeling, Dimensional, Image Processing