Papers › Comparing distributions: \ell_1 geometry improves kernel two-sample testing

Comparing distributions: \ell_1 geometry improves kernel two-sample testing

1 Dec 2019NeurIPS 2019 12archive 2025-07-28

Meyer Scetbon, Gael Varoquaux

Are two sets of observations drawn from the same distribution? This problem is a two-sample test. Kernel methods lead to many appealing properties. Indeed state-of-the-art approaches use the L² distance between kernel-based distribution representatives to derive their test statistics. Here, we show that Lᵖ distances (with p≥1) between these distribution representatives give metrics on the space of distributions that are well-behaved to detect differences between distributions as they metrize the weak convergence. Moreover, for analytic kernels, we show that the L¹ geometry gives improved testing power for scalable computational procedures. Specifically, we derive a finite dimensional approximation of the metric given as the ℓ₁ norm of a vector which captures differences of expectations of analytic functions evaluated at spatial locations or frequencies (i.e, features). The features can be chosen to maximize the differences of the distributions and give interpretable indications of how they differs. Using an ℓ₁ norm gives better detection because differences between representatives are dense as we use analytic kernels (non-zero almost everywhere). The tests are consistent, while much faster than state-of-the-art quadratic-time kernel-based tests. Experiments on artificial and real-world problems demonstrate improved power/time tradeoff than the state of the art, based on ℓ₂ norms, and in some cases, better outright power than even the most expensive quadratic-time tests. This performance gain is retained even in high dimensions.

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Two-sample testingVocal Bursts Valence Prediction

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