Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Details Gaussian Mixture Modeling with Gaussian Process Latent Variable Models Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

2010-06-18

arxiv.org/abs/1006.3640v2

Statistics

Machine Learning

11 pages, 2 figures, 3 tables

Scientific paper

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.

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