Ricci Curvature and the Manifold Learning Problem
Not on the map. Why?
Abstract (arXiv)
Consider a sample of n points taken i.i.d from a submanifold Sigma of Euclidean space. We show that there is a way to estimate the Ricci curvature of Sigma with respect to the induced metric from the sample. Our method is grounded in the notions of Carr'e du Champ for diffusion semi-groups, the theory of Empirical processes and local Principal Component Analysis.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Oct 2014.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2022kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learningcited by 14
- 2021Model-centric Data Manifold: the Data Through the Eyes of the Modelcited by 9
- 2024Geometry of Singular Foliations and Learning Manifolds in ReLU Networks via the Data Information Matrixcited by 1
- 2026The Data Manifold under the Microscopecited by 0
- 2026The Data Manifold under the Microscope14 Jun 2026
- 2024Geometry of Singular Foliations and Learning Manifolds in ReLU Networks via the Data Information Matrix11 Sep 2024
- 2022kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning4 Dec 2022
- 2021Model-centric Data Manifold: the Data Through the Eyes of the Model26 Apr 2021