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Conference Paper

Tailoring density estimation via reproducing kernel moment matching

MPS-Authors
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Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://dl.acm.org/citation.cfm?doid=1390156.1390281
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Citation

Song, L., Zhang, X., Smola, A., Gretton, A., & Schölkopf, B. (2008). Tailoring density estimation via reproducing kernel moment matching. In W. Cohen, A. McCallum, & S. Roweis (Eds.), ICML '08: Proceedings of the 25th international conference on Machine learning (pp. 992-999). New York, NY, USA: ACM Press.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C843-B
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 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.