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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 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.
