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

TL;DR

This paper introduces an $ ext{L}^p$-based approach, especially $ ext{L}^1$, for kernel two-sample testing that improves detection power and computational efficiency by leveraging the geometry of distribution representations.

Contribution

It demonstrates that $ ext{L}^p$ distances with $p eq 2$ provide well-behaved metrics for distribution comparison and introduces a scalable $ ext{L}^1$-based test with finite-dimensional approximation.

Findings

$ ext{L}^1$ geometry improves detection power for analytic kernels.

Proposed method is faster and more powerful than state-of-the-art quadratic-time tests.

Experiments show better power/time tradeoff and sometimes superior detection than existing methods.

Abstract

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 $L^2$ distance between kernel-based distribution representatives to derive their test statistics. Here, we show that $L^p$ distances (with $p\geq 1$) 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 $L^1$ geometry gives improved testing power for scalable computational procedures. Specifically, we derive a finite dimensional approximation of the metric given as the $\ell_1$ 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 $\ell_1$ 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 $\ell_2$ norms, and in some cases, better outright power than even the most expensive quadratic-time tests.