Embeddings of Persistence Diagrams into Hilbert Spaces
Not on the map. Why?
Abstract (arXiv)
Since persistence diagrams do not admit an inner product structure, a map into a Hilbert space is needed in order to use kernel methods. It is natural to ask if such maps necessarily distort the metric on persistence diagrams. We show that persistence diagrams with the bottleneck distance do not even admit a coarse embedding into a Hilbert space. As part of our proof, we show that any separable, bounded metric space isometrically embeds into the space of persistence diagrams with the bottleneck distance. As corollaries, we obtain the generalized roundness, negative type, and asymptotic dimension of this space.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2019.
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Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 0; 2025: 0; 2026: 3.
11 papers.
- 2022RipsNet: a general architecture for fast and robust estimation of the persistent homology of point cloudscited by 20
- 2023Euler Characteristic Tools For Topological Data Analysiscited by 13
- 2021From Geometry to Topology: Inverse Theorems for Distributed Persistencecited by 10
- 2021Estimation and Quantization of Expected Persistence Diagramscited by 8
- 2022Approximating Persistent Homology for Large Datasetscited by 8
- 2021Topology Applied to Machine Learning: From Global to Localcited by 4
- 2026A Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classificationcited by 3
- 2023Vector Summaries of Persistence Diagrams for Permutation-based Hypothesis Testingcited by 1
- 2022Convolutional Persistence Transformscited by 0
- 2026Revisiting the Sliced Wasserstein Kernel for persistence diagrams: a Figalli-Gigli approachcited by 0
- 2026LandscapeSHAP: Which Persistent Homology Class Gets the Credit?cited by 0
- 2026LandscapeSHAP: Which Persistent Homology Class Gets the Credit?25 Sep 2026
- 2026A Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classification4 May 2026
- 2026Revisiting the Sliced Wasserstein Kernel for persistence diagrams: a Figalli-Gigli approach6 Feb 2026
- 2023Vector Summaries of Persistence Diagrams for Permutation-based Hypothesis Testing9 Jun 2023
- 2023Euler Characteristic Tools For Topological Data Analysis24 Mar 2023
- 2022Convolutional Persistence Transforms3 Aug 2022
- 2022Approximating Persistent Homology for Large Datasets19 Apr 2022
- 2022RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds3 Feb 2022
- 2021Estimation and Quantization of Expected Persistence Diagrams11 May 2021
- 2021Topology Applied to Machine Learning: From Global to Local10 Mar 2021
- 2021From Geometry to Topology: Inverse Theorems for Distributed Persistence28 Jan 2021