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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. In M. Goesele, S. Roth, A. Kuijper, B. Schiele, & K. Schindler (Eds.), DAGM 2010: Pattern Recognition (pp. 271-282). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BE68-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-7F25-7
Genre: Conference Paper

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 Creators:
Nickisch, H1, 2, Author              
Rasmussen, CE1, 2, Author              
Affiliations:
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: 2010-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1007/978-3-642-15986-2_28
BibTex Citekey: 6716
 Degree: -

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Title: 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM 2010)
Place of Event: Darmstadt, Germany
Start-/End Date: 2010-09-22 - 2010-09-24

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Source 1

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Title: DAGM 2010: Pattern Recognition
Source Genre: Proceedings
 Creator(s):
Goesele, M, Editor
Roth, S, Editor
Kuijper, A, Editor
Schiele, B, Editor
Schindler, K, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 271 - 282 Identifier: ISBN: 978-3-642-15986-2

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 6376 Sequence Number: - Start / End Page: - Identifier: -