Unconstrained and Curvature-Constrained Shortest-Path Distances and their Approximation
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Abstract (arXiv)
We study shortest paths and their distances on a subset of a Euclidean space, and their approximation by their equivalents in a neighborhood graph defined on a sample from that subset. In particular, we recover and extend the results of Bernstein et al. (2000). We do the same with curvature-constrained shortest paths and their distances, establishing what we believe are the first approximation bounds for them.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2017.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1.
4 papers.
- 2021Non-Parametric Manifold Learningcited by 0
- 2022On the Selection of Tuning Parameters for Patch-Stitching Embedding Methodscited by 0
- 2022Embedding Functional Data: Multidimensional Scaling and Manifold Learningcited by 0
- 2024Semi-supervised Fr'echet Regressioncited by 0
- 2024Semi-supervised Fr'echet Regression16 Apr 2024
- 2022Embedding Functional Data: Multidimensional Scaling and Manifold Learning30 Aug 2022
- 2022On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods14 Jul 2022
- 2021Non-Parametric Manifold Learning16 Jul 2021