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Testing for Homogeneity with Kernel Fisher Discriminant Analysis AbstractWe propose to investigate test statistics for testing homogeneity based on kernel Fisher discriminant analysis. Asymptotic null distribution under null hypothesis is derived, and consistency against fixed alternatives is assessed. Finally, experimental evidence of the performance of the proposed approach on artificial data and on a speaker verification task is provided.
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