English

Exploring Singularities in point clouds with the graph Laplacian: An explicit approach

Machine Learning 2026-02-24 v4 Machine Learning Differential Geometry

Abstract

We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of datasets. Our theory provides theoretical guarantees and explicit bounds on the functional forms of the graph Laplacian when it acts on functions defined close to singularities of the underlying manifold. We use these explicit bounds to develop tests for singularities and propose methods that can be used to estimate geometric properties of singularities in the datasets.

Keywords

Cite

@article{arxiv.2301.00201,
  title  = {Exploring Singularities in point clouds with the graph Laplacian: An explicit approach},
  author = {Martin Andersson and Benny Avelin},
  journal= {arXiv preprint arXiv:2301.00201},
  year   = {2026}
}

Comments

28 pages, 12 figures

R2 v1 2026-06-28T07:58:12.781Z