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Tailoring density estimation via reproducing kernel moment matching

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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;

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

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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: http://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.