ML Anthology

A Hilbert Space Embedding for Distributions

Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf
ALT 2007 pp. 13-31
doi:10.1007/978-3-540-75225-7_5 /alt/2007/smola2007alt-hilbert/

Abstract

We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.

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Text

Smola et al. "A Hilbert Space Embedding for Distributions." International Conference on Algorithmic Learning Theory, 2007. doi:10.1007/978-3-540-75225-7_5

Markdown

[Smola et al. "A Hilbert Space Embedding for Distributions." International Conference on Algorithmic Learning Theory, 2007.](https://mlanthology.org/alt/2007/smola2007alt-hilbert/) doi:10.1007/978-3-540-75225-7_5

BibTeX

@inproceedings{smola2007alt-hilbert,
  title     = {{A Hilbert Space Embedding for Distributions}},
  author    = {Smola, Alexander J. and Gretton, Arthur and Song, Le and Schölkopf, Bernhard},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {2007},
  pages     = {13-31},
  doi       = {10.1007/978-3-540-75225-7_5},
  url       = {https://mlanthology.org/alt/2007/smola2007alt-hilbert/}
}