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

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

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BFAA-E Version Permalink: https://hdl.handle.net/21.11116/0000-0002-8592-2

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https://arxiv.org/abs/1006.3640 (Any fulltext) Open Access status unknown

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Nickisch, H^{1, 2}, Author
Rasmussen, CE, Author

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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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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.

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Dates: Date issued: 2010-06

Publication Status: Issued

Pages: 10

Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics

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Identifiers: BibTex Citekey: 6634

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Title: Technical Report of the Max Planck Institute for Biological Cybernetics

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