Header logo is ei

Tailoring density estimation via reproducing kernel moment matching

2008

Conference Paper

ei


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

Links: PDF
PDF

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},
  month_numeric = {7}
}