Song, Le; Zhang, Xinhua; Smola, Alexander; Gretton, Arthur; Schoelkopf, Bernhard
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...[Show more]
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