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Tailoring density estimation via reproducing kernel moment matching

Song, L; Zhang, X; Smola, A; Gretton, A; Schölkopf, B; (2008) Tailoring density estimation via reproducing kernel moment matching. In: (pp. pp. 992-999).

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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. Copyright 2008 by the author(s)/owner(s).

Type: Proceedings paper
Title: Tailoring density estimation via reproducing kernel moment matching
ISBN-13: 9781605582054
URI: http://discovery.ucl.ac.uk/id/eprint/1334319
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