A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification
Abstract
We introduce PALACE (Persistence Adaptive-Landmark Analytic Classification Engine), the data-adaptive companion to PLACE, paying a small cross-validation tier on three knobs (budget, radii, bandwidth; $\leq 5$ choices each). A cover-theoretic core (Lebesgue-number criterion on the landmark cover) yields four closed-form guarantees. (i) A structural lower distortion bound $λ(τ;ν)$ on $\mathcal{D}_n$ under cross-diagram non-interference, with a $(D/L)^2$ budget reduction over the uniform grid when...
Description / Details
We introduce PALACE (Persistence Adaptive-Landmark Analytic Classification Engine), the data-adaptive companion to PLACE, paying a small cross-validation tier on three knobs (budget, radii, bandwidth; choices each). A cover-theoretic core (Lebesgue-number criterion on the landmark cover) yields four closed-form guarantees. (i) A structural lower distortion bound on under cross-diagram non-interference, with a budget reduction over the uniform grid when diagrams concentrate. (ii) Equal weights maximizing , and farthest-point-sampling positions -approximating the optimal -center covering radius; both derived from training labels alone, no gradient training. (iii) A kernel-RKHS classification rate with binary necessity threshold from a matching Le Cam lower bound, and a closed-form filtration-selection rule. The kernel-Mahalanobis margin is the strongest closed-form ranker across the chemical-graph pool (mean Spearman ); the isotropic surrogate admits a selection-consistency rate, and from (i) provides an independent data-level signal (positive on COX2 and PTC). (iv) A per-prediction certificate, in non-asymptotic Pinelis and asymptotic Gaussian forms, with no calibration split. Empirically, PALACE is the strongest closed-form diagram-based method on Orbit5k (, matching Persformer), leads every diagram-based competitor on COX2 and MUTAG, and is competitive on DHFR (within 1 pp of ECP). At domain inflation, adaptive placement maintains while the uniform grid collapses to chance ( on 4-class data).
Source: arXiv:2605.04046v1 - http://arxiv.org/abs/2605.04046v1 PDF: https://arxiv.org/pdf/2605.04046v1 Original Link: http://arxiv.org/abs/2605.04046v1
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May 6, 2026
Data Science
Machine Learning
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