Nonembeddability of Persistence Diagrams with p>2 Wasserstein Metric
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Abstract (arXiv)
Persistence diagrams do not admit an inner product structure compatible with any Wasserstein metric. Hence, when applying kernel methods to persistence diagrams, the underlying feature map necessarily causes distortion. We prove persistence diagrams with the p-Wasserstein metric do not admit a coarse embedding into a Hilbert space when p > 2.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Oct 2019.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2021From Geometry to Topology: Inverse Theorems for Distributed Persistencecited by 10
- 2021Estimation and Quantization of Expected Persistence Diagramscited by 8
- 2022Convolutional Persistence Transformscited by 0
- 2022Convolutional Persistence Transforms3 Aug 2022
- 2021Estimation and Quantization of Expected Persistence Diagrams11 May 2021
- 2021From Geometry to Topology: Inverse Theorems for Distributed Persistence28 Jan 2021