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DTM-based filtrations

  • Hirokazu Anai
  • , Frédéric Chazal
  • , Marc Glisse
  • , Yuichi Ike
  • , Hiroya Inakoshi
  • , Raphaël Tinarrage
  • , Yuhei Umeda
  • National Institute for Environmental Studies
  • INRIA Institut National de Recherche en Informatique et en Automatique

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Despite strong stability properties, the persistent homology of filtrations classically used in Topological Data Analysis, such as, e.g. the Čech or Vietoris-Rips filtrations, are very sensitive to the presence of outliers in the data from which they are computed. In this paper, we introduce and study a new family of filtrations, the DTM-filtrations, built on top of point clouds in the Euclidean space which are more robust to noise and outliers. The approach adopted in this work relies on the notion of distance-to-measure functions and extends some previous work on the approximation of such functions.

Original languageEnglish
Title of host publication35th International Symposium on Computational Geometry, SoCG 2019
EditorsGill Barequet, Yusu Wang
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959771047
DOIs
Publication statusPublished - 1 Jun 2019
Externally publishedYes
Event35th International Symposium on Computational Geometry, SoCG 2019 - Portland, United States
Duration: 18 Jun 201921 Jun 2019

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume129
ISSN (Print)1868-8969

Conference

Conference35th International Symposium on Computational Geometry, SoCG 2019
Country/TerritoryUnited States
CityPortland
Period18/06/1921/06/19

Keywords

  • Persistent homology
  • Topological Data Analysis

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