Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Nickisch, H; Rasmussen, CE

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Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

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Nickisch, H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://arxiv.org/abs/1006.3640
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Citation

Nickisch, H., & Rasmussen, C.(2010). Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BFAA-E

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.