Abstract
In topological data analysis, the matching distance is a computationally tractable metric on multifiltered simplicial complexes. We design efficient algorithms for approximating the matching distance of two bi-filtered complexes to any desired precision ε > 0. Our approach is based on a quad-tree refinement strategy introduced by Biasotti et al., but we recast their approach entirely in geometric terms. This point of view leads to several novel observations resulting in a practically faster algorithm. We demonstrate this speed-up by experimental comparison and provide our code in a public repository which provides the first efficient publicly available implementation of the matching distance.
Originalsprache | englisch |
---|---|
Titel | 36th International Symposium on Computational Geometry, SoCG 2020 |
Redakteure/-innen | Sergio Cabello, Danny Z. Chen |
Erscheinungsort | Wadern |
Herausgeber (Verlag) | Schloss Dagstuhl - Leibniz-Zentrum für Informatik |
Seitenumfang | 16 |
ISBN (elektronisch) | 9783959771436 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2020 |
Veranstaltung | 36th International Symposium on Computational Geometry: SoCG 2020 - Zürich, Virtuell, Schweiz Dauer: 23 Juni 2020 → 26 Juni 2020 https://socg20.inf.ethz.ch/socg |
Publikationsreihe
Name | Leibniz International Proceedings in Informatics, LIPIcs |
---|---|
Band | 164 |
ISSN (Print) | 1868-8969 |
Konferenz
Konferenz | 36th International Symposium on Computational Geometry |
---|---|
Kurztitel | CG Week 2020 |
Land/Gebiet | Schweiz |
Ort | Virtuell |
Zeitraum | 23/06/20 → 26/06/20 |
Internetadresse |
ASJC Scopus subject areas
- Software