Laplace Learning in Wasserstein Space

• URL: http://arxiv.org/abs/2511.13229v1
• Date: Mon, 17 Nov 2025 10:49:36 GMT
• Title: Laplace Learning in Wasserstein Space
• Authors: Mary Chriselda Antony Oliver, Michael Roberts, Carola-Bibiane Schönlieb, Matthew Thorpe,
• Abstract summary: We assume manifold hypothesis to investigate graph-based semi-supervised learning methods.<n>In particular, we examine Laplace Learning in the Wasserstein space.<n>We prove variational convergence of a discrete graph p- Dirichlet energy to its continuum counterpart.
• Score: 20.33446919989862
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The manifold hypothesis posits that high-dimensional data typically resides on low-dimensional sub spaces. In this paper, we assume manifold hypothesis to investigate graph-based semi-supervised learning methods. In particular, we examine Laplace Learning in the Wasserstein space, extending the classical notion of graph-based semi-supervised learning algorithms from finite-dimensional Euclidean spaces to an infinite-dimensional setting. To achieve this, we prove variational convergence of a discrete graph p- Dirichlet energy to its continuum counterpart. In addition, we characterize the Laplace-Beltrami operator on asubmanifold of the Wasserstein space. Finally, we validate the proposed theoretical framework through numerical experiments conducted on benchmark datasets, demonstrating the consistency of our classification performance in high-dimensional settings.

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