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FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test
The maximum mean discrepancy (MMD) is a recently proposed test statistic for two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner's theorem and Fourierdoi:10.1162/neco_a_00732 pmid:25774545 fatcat:za5noapdqbglbmziq6ddmzfru4