Testing for Homogeneity with Kernel Fisher Discriminant Analysis
Machine Learning
2008-12-18 v1
Abstract
We propose to investigate test statistics for testing homogeneity in reproducing kernel Hilbert spaces. Asymptotic null distributions under null hypothesis are derived, and consistency against fixed and local alternatives is assessed. Finally, experimental evidence of the performance of the proposed approach on both artificial data and a speaker verification task is provided.
Cite
@article{arxiv.0804.1026,
title = {Testing for Homogeneity with Kernel Fisher Discriminant Analysis},
author = {Zaid Harchaoui and Francis Bach and Eric Moulines},
journal= {arXiv preprint arXiv:0804.1026},
year = {2008}
}