Statistical Analysis of Persistence Intensity Functions
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Abstract (arXiv)
Persistence diagrams are two-dimensional plots that summarize the topological features of functions and are an important part of topological data analysis. A problem that has received much attention is how deal with sets of persistence diagrams. How do we summarize them, average them or cluster them? One approach -- the persistence intensity function -- was introduced informally by Edelsbrunner, Ivanov, and Karasev (2012). Here we provide a modification and formalization of this approach. Using the persistence intensity function, we can visualize multiple diagrams, perform clustering and conduct two-sample tests.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Oct 2015.
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Citing papers per year: 2021: 5; 2022: 0; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
8 papers.
- 2021TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Imagescited by 12
- 2021Persistent Homology and Graphs Representation Learningcited by 5
- 2021Topology Applied to Machine Learning: From Global to Localcited by 4
- 2021A computationally efficient framework for vector representation of persistence diagramscited by 3
- 2021A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clusteringcited by 2
- 2023Vector Summaries of Persistence Diagrams for Permutation-based Hypothesis Testingcited by 1
- 2026Persistence-based topological optimization: a surveycited by 1
- 2026Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budgetcited by 0
- 2026Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budget6 Oct 2026
- 2026Persistence-based topological optimization: a survey24 Mar 2026
- 2023Vector Summaries of Persistence Diagrams for Permutation-based Hypothesis Testing9 Jun 2023
- 2021A computationally efficient framework for vector representation of persistence diagrams16 Sep 2021
- 2021A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering25 May 2021
- 2021Topology Applied to Machine Learning: From Global to Local10 Mar 2021
- 2021Persistent Homology and Graphs Representation Learning25 Feb 2021
- 2021TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Images21 Jan 2021