Time-adaptive infinite-dimensional Gaussian process regression on manifolds

Abstract:This paper proposes a new formulation of functional Gaussian Process regression on manifolds, based on an Empirical Bayes approach, in the spatiotemporal random field context. We apply the machinery of tight Gaussian measures in separable Hilbert spaces, exploiting the invariance property of covariance kernels under the group of isometries of the manifold. The identification via characteristic function of these measures with the infinite product of one-dimensional Gaussian measures is then obtained, in terms of the eigenfunctions of the Laplace-Beltrami operator on the manifold. The involved time-varying angular spectrum constitutes the key tool for dimension reduction in the implementation of this regression approach, adopting a suitable truncation scheme depending on the functional sample size. The simulation study and synthetic data application illustrate the performance of the proposed functional regression predictor.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2603.21144 [stat.ML]

(or arXiv:2603.21144v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2603.21144 Focus to learn more arXiv-issued DOI via DataCite

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