Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
2010
Conference Paper
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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.
Author(s): | Nickisch, H. and Rasmussen, CE. |
Book Title: | Pattern Recognition |
Journal: | Pattern Recognition: 32nd DAGM Symposium |
Pages: | 271-282 |
Year: | 2010 |
Month: | September |
Day: | 0 |
Editors: | Goesele, M. , S. Roth, A. Kuijper, B. Schiele, K. Schindler |
Publisher: | Springer |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1007/978-3-642-15986-2_28 |
Event Name: | 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM 2010) |
Event Place: | Darmstadt, Germany |
Address: | Berlin, Germany |
Digital: | 0 |
Institution: | Deutsche Arbeitsgemeinschaft für Mustererkennung |
ISBN: | 978-3-642-15986-2 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{6716, title = {Gaussian Mixture Modeling with Gaussian Process Latent Variable Models}, author = {Nickisch, H. and Rasmussen, CE.}, journal = {Pattern Recognition: 32nd DAGM Symposium}, booktitle = {Pattern Recognition}, pages = {271-282}, editors = {Goesele, M. , S. Roth, A. Kuijper, B. Schiele, K. Schindler}, publisher = {Springer}, organization = {Max-Planck-Gesellschaft}, institution = {Deutsche Arbeitsgemeinschaft für Mustererkennung}, school = {Biologische Kybernetik}, address = {Berlin, Germany}, month = sep, year = {2010}, doi = {10.1007/978-3-642-15986-2_28}, month_numeric = {9} } |