Tailoring density estimation via reproducing kernel moment matching

Le Song*, Xinhua Zhang, Alex Smola, Arthur Gretton, Bernhard Schölkopf

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

43 Citations (Scopus)

Abstract

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 Hubert 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.

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Machine Learning
PublisherAssociation for Computing Machinery (ACM)
Pages992-999
Number of pages8
ISBN (Print)9781605582054
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event25th International Conference on Machine Learning - Helsinki, Finland
Duration: 5 Jul 20089 Jul 2008

Publication series

NameProceedings of the 25th International Conference on Machine Learning

Conference

Conference25th International Conference on Machine Learning
Country/TerritoryFinland
CityHelsinki
Period5/07/089/07/08

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