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ICML
2008
IEEE

Tailoring density estimation via reproducing kernel moment matching

13 years 21 days ago
Tailoring density estimation via reproducing kernel moment matching
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.
Alex J. Smola, Arthur Gretton, Bernhard Schöl
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2008
Where ICML
Authors Alex J. Smola, Arthur Gretton, Bernhard Schölkopf, Le Song, Xinhua Zhang
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