Abstract:We prove that kernel covariance embeddings lead to information-theoretically perfect separation of distinct continuous probability distributions. In statistical terms, we establish that testing for the \emph{equality} of two non-atomic (Borel) probability measures on a locally compact uncountable Polish space is \emph{equivalent} to testing for the \emph{singularity} between two centered Gaussian measures on a reproducing kernel Hilbert space. The corresponding Gaussians are defined via the notion of kernel covariance embedding of a probability measure, and the Hilbert space is that generated by the embedding kernel. Distinguishing singular Gaussians is structurally simpler from an information-theoretic perspective than non-parametric two-sample testing, particularly in complex or high-dimensional domains. This is because singular Gaussians are supported on essentially separate and affine subspaces. Our proof leverages the classical Feldman-Hájek dichotomy, and shows that even a small perturbation of a continuous distribution will be maximally magnified through its Gaussian embedding. This ``separation of measure phenomenon'' appears to be a blessing of infinite dimensionality, by means of embedding, with the potential to inform the design of efficient inference tools in considerable generality. The elicitation of this phenomenon also appears to crystallize, in a precise and simple mathematical statement, a core mechanism underpinning the empirical effectiveness of kernel methods.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST) |
| MSC classes: | 62G10, 46E22, 60G15 |
| Cite as: | arXiv:2505.04613 [stat.ML] |
| (or arXiv:2505.04613v4 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2505.04613 arXiv-issued DOI via DataCite |
Submission history
From: Leonardo V. Santoro [view email]
[v1]
Wed, 7 May 2025 17:56:19 UTC (19 KB)
[v2]
Mon, 15 Sep 2025 09:35:15 UTC (592 KB)
[v3]
Fri, 23 Jan 2026 16:38:50 UTC (771 KB)
[v4]
Wed, 13 May 2026 10:28:22 UTC (1,367 KB)