Bottleneck profiles and discrete Prokhorov metrics for persistence diagrams

DOI10.1007/s00454-023-00498-warXiv2106.02538OpenAlexW3166214348MaRDI QIDQ6204774

Paweł Dłotko, Niklas Hellmer

Publication date: 2 April 2024

Published in: Discrete \& Computational Geometry (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2106.02538

zbMATH Keywords

topological data analysispersistent homologyProkhorov distance

Mathematics Subject Classification ID

Persistent homology and applications, topological data analysis (55N31) Computational aspects of data analysis and big data (68T09) Topological data analysis (62R40)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Computational Optimal Transport: With Applications to Data Science
• Robust statistics, hypothesis testing, and confidence intervals for persistent homology on metric measure spaces
• A new entropy based summary function for topological data analysis
• Topological spaces of persistence modules and their properties
• Probability measures on the space of persistence diagrams
• Geometry Helps to Compare Persistence Diagrams
• On Choosing and Bounding Probability Metrics
• Decomposition of pointwise finite-dimensional persistence modules
• Classical Fourier Analysis
• Metrics and Stabilization in One Parameter Persistence
• Persistence Images: A Stable Vector Representation of Persistent Homology
• An $n^{5/2} $ Algorithm for Maximum Matchings in Bipartite Graphs
• Metric structures for Riemannian and non-Riemannian spaces. Transl. from the French by Sean Michael Bates. With appendices by M. Katz, P. Pansu, and S. Semmes. Edited by J. LaFontaine and P. Pansu
• Geometry helps in bottleneck matching and related problems

This page was built for publication: Bottleneck profiles and discrete Prokhorov metrics for persistence diagrams