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A Hilbert Space Embedding for Distributions

2007

Conference Paper

ei


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.

Author(s): Smola, A. and Gretton, A. and Song, L. and Schölkopf, B.
Book Title: Algorithmic Learning Theory, Lecture Notes in Computer Science 4754
Journal: Algorithmic Learning Theory: 18th International Conference (ALT 2007)
Pages: 13-31
Year: 2007
Month: October
Day: 0
Editors: M Hutter and RA Servedio and E Takimoto
Publisher: Springer

Department(s): Empirische Inferenz
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-540-75225-7_5
Event Name: 18th International Conference on Algorithmic Learning Theory (ALT 2007)
Event Place: Sendai, Japan

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{4645,
  title = {A Hilbert Space Embedding for Distributions},
  author = {Smola, A. and Gretton, A. and Song, L. and Sch{\"o}lkopf, B.},
  journal = {Algorithmic Learning Theory: 18th International Conference (ALT 2007)},
  booktitle = {Algorithmic Learning Theory, Lecture Notes in Computer Science 4754 },
  pages = {13-31},
  editors = {M Hutter and RA Servedio and E Takimoto},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = oct,
  year = {2007},
  month_numeric = {10}
}