mlsanityData to 14 Oct 2026

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

arXiv 2301.00201 ↗First posted 31 Dec 2022stat.ML cs.LG math.DGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 31 Dec 2022.

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3 of 24 references.

References · 3

  1. 2018Gradient Descent Provably Optimizes Over-parameterized Neural Networkscited by 659
  2. 2019Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networkscited by 566
  3. 2020Approximation of BV functions by neural networks: A regularity theory approachcited by 2

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