Kernel Two-Sample Testing via Directional Components Analysis

Rui Cui; Yuhao Li; Xiaojun Song

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arXiv · 2508.08564

Kernel Two-Sample Testing via Directional Components Analysis

Rui Cui·Yuhao Li·Xiaojun Song

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Abstract

Standard kernel two-sample tests, such as those based on the Maximum Mean Discrepancy (MMD), aggregate squared differences across all directions in a Reproducing Kernel Hilbert Space (RKHS). However, in finite samples, trailing directional components are noisy, which degrades test power. We propose a novel kernel-based test that resolves this by truncating the spectral decomposition of the MMD, retaining only the well-estimated leading eigen-directions. By aggregating these robust components, our method achieves superior power and robustness, particularly in high-dimensional and unbalanced settings. Furthermore, we introduce a computationally efficient parametric bootstrap procedure for approximating critical values, which is theoretically justified and significantly faster than permutation-based alternatives. Extensive simulations and empirical studies demonstrate that our method maintains strict Type I error control while delivering higher power than existing MMD-based tests.

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BibTeX RIS

Rui Cui, Yuhao Li, Xiaojun Song. 2025-08-12. Kernel Two-Sample Testing via Directional Components Analysis. https://arxiv.org/abs/2508.08564

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