A Kernel Method for the Two-Sample-Problem

Gretton, A; Borgwardt, KM; Rasch, M; Schölkopf, B; Smola, A

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

A Kernel Method for the Two-Sample-Problem

MPS-Authors

/persons/resource/persons83946

Gretton, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://papers.nips.cc/paper/3110-a-kernel-method-for-the-two-sample-problem.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., & Smola, A. (2007). A Kernel Method for the Two-Sample-Problem. In B. Schölkopf, J. Platt, & T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19 (pp. 513-520). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CBCB-3

Abstract

We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is
based on the asymptotic distribution of this statistic.
The test statistic can be computed in O(m^2) time. We apply our approach to a variety of problems, including attribute matching for databases using the Hungarian marriage method, where our test performs strongly.
We also demonstrate excellent performance when comparing distributions over graphs, for which no alternative tests currently exist.