Tailoring density estimation via reproducing kernel moment matching
2008
Conference Paper
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Moment matching is a popular means of parametric density estimation. We extend this technique to nonparametric estimation of mixture models. Our approach works by embedding distributions into a reproducing kernel Hilbert space, and performing moment matching in that space. This allows us to tailor density estimators to a function class of interest (i.e., for which we would like to compute expectations). We show our density estimation approach is useful in applications such as message compression in graphical models, and image classification and retrieval.
Author(s): | Song, L. and Zhang, X. and Smola, A. and Gretton, A. and Schölkopf, B. |
Book Title: | Proceedings of the 25th International Conference onMachine Learning |
Pages: | 992-999 |
Year: | 2008 |
Month: | July |
Day: | 0 |
Editors: | WW Cohen and A McCallum and S Roweis |
Publisher: | ACM Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1145/1390156.1390281 |
Event Name: | ICML 2008 |
Event Place: | Helsinki, Finland |
Address: | New York, NY, USA |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @inproceedings{5155, title = {Tailoring density estimation via reproducing kernel moment matching}, author = {Song, L. and Zhang, X. and Smola, A. and Gretton, A. and Sch{\"o}lkopf, B.}, booktitle = {Proceedings of the 25th International Conference onMachine Learning}, pages = {992-999}, editors = {WW Cohen and A McCallum and S Roweis}, publisher = {ACM Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {New York, NY, USA}, month = jul, year = {2008}, doi = {10.1145/1390156.1390281}, month_numeric = {7} } |