ML Anthology

Testing for Homogeneity with Kernel Fisher Discriminant Analysis

Moulines Eric, Francis R. Bach, Zaïd Harchaoui
NeurIPS 2007 pp. 609-616
/neurips/2007/eric2007neurips-testing/

Abstract

We propose to test for the homogeneity of two samples by using Kernel Fisher discriminant Analysis. This provides us with a consistent nonparametric test statistic, for which we derive the asymptotic distribution under the null hypothesis. We give experimental evidence of the relevance of our method on both artificial and real datasets.

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Cite

Text

Eric et al. "Testing for Homogeneity with Kernel Fisher Discriminant Analysis." Neural Information Processing Systems, 2007.

Markdown

[Eric et al. "Testing for Homogeneity with Kernel Fisher Discriminant Analysis." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/eric2007neurips-testing/)

BibTeX

@inproceedings{eric2007neurips-testing,
  title     = {{Testing for Homogeneity with Kernel Fisher Discriminant Analysis}},
  author    = {Eric, Moulines and Bach, Francis R. and Harchaoui, Zaïd},
  booktitle = {Neural Information Processing Systems},
  year      = {2007},
  pages     = {609-616},
  url       = {https://mlanthology.org/neurips/2007/eric2007neurips-testing/}
}