@inproceedings{7fad7000979e496599dca7c600c6c08f,
title = "Tailoring density estimation via reproducing kernel moment matching",
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.",
author = "Le Song and Xinhua Zhang and Alex Smola and Arthur Gretton and Bernhard Sch{\"o}lkopf",
year = "2008",
doi = "10.1145/1390156.1390281",
language = "English",
isbn = "9781605582054",
series = "Proceedings of the 25th International Conference on Machine Learning",
publisher = "Association for Computing Machinery (ACM)",
pages = "992--999",
booktitle = "Proceedings of the 25th International Conference on Machine Learning",
address = "United States",
note = "25th International Conference on Machine Learning ; Conference date: 05-07-2008 Through 09-07-2008",
}