Testing for Homogeneity with Kernel Fisher Discriminant Analysis

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Moulines Eric, Francis Bach, Zaïd Harchaoui


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.