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2007 NeurIPS NeurIPS 2007

Testing for Homogeneity with Kernel Fisher Discriminant Analysis

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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Mathematics & Optimization
📈 Trend Setter - Estimation
🧭 Keyword Pioneer - discriminant analysis
🐣 Hot Topic Early Bird - hypothesis testing

Authors

Moulines Eric, Francis R. Bach, Zaïd Harchaoui

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Optimization & Theory > Theory Mathematics & Optimization > Mathematics > Statistics Mathematics & Optimization > Statistics Machine Learning > Optimization & Theory > Statistics Machine Learning > Core Methods > Kernel Methods Machine Learning > Core Methods > Estimation

Keywords

statistical learning hypothesis testing statistical testing two-sample test kernel fisher discriminant homogeneity testing nonparametric statistics discriminant analysis kernel fisher discriminant analysis asymptotic distribution nonparametric test kernel methods homogeneity test
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