Large sample analysis of the median heuristic

Damien Garreau, Motonobu Kanagawa, Wittawat Jitkrittum

Publication date: 23 July 2017

Abstract: In kernel methods, the median heuristic has been widely used as a way of setting the bandwidth of RBF kernels. While its empirical performances make it a safe choice under many circumstances, there is little theoretical understanding of why this is the case. Our aim in this paper is to advance our understanding of the median heuristic by focusing on the setting of kernel two-sample test. We collect new findings that may be of interest for both theoreticians and practitioners. In theory, we provide a convergence analysis that shows the asymptotic normality of the bandwidth chosen by the median heuristic in the setting of kernel two-sample test. Systematic empirical investigations are also conducted in simple settings, comparing the performances based on the bandwidths chosen by the median heuristic and those by the maximization of test power.

Has companion code repository: https://github.com/mmontana/ECG-heartbeat-classification

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Order statistics; empirical distribution functions (62G30)

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