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...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of The 25th International Conference on Machine Learning (ICML 2008)|
|01_Song_Tailoring_density_estimation_2008.pdf||1.77 MB||Adobe PDF||Request a copy|
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