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Intrinsic topological transforms via the distance kernel embedding

  • INRIA
  • INRIA
  • Duke University

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

Abstract

Topological transforms are parametrized families of topological invariants, which, by analogy with transforms in signal processing, are much more discriminative than single measurements. The first two topological transforms to be defined were the Persistent Homology Transform (PHT) and Euler Characteristic Transform (ECT), both of which apply to shapes embedded in Euclidean space. The contribution of this paper is to define topological transforms for abstract metric measure spaces. Our proposed pipeline is to pre-compose the PHT or ECT with a Euclidean embedding derived from the eigenfunctions and eigenvalues of an integral operator. To that end, we define and study an integral operator called the distance kernel operator, and demonstrate that it gives rise to stable and quasi-injective topological transforms. We conclude with some numerical experiments, wherein we compute and compare the eigenfunctions and eigenvalues of our operator across a range of standard 2- and 3-manifolds.

Original languageEnglish
Title of host publication36th International Symposium on Computational Geometry, SoCG 2020
EditorsSergio Cabello, Danny Z. Chen
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959771436
DOIs
Publication statusPublished - 1 Jun 2020
Externally publishedYes
Event36th International Symposium on Computational Geometry, SoCG 2020 - Zurich, Switzerland
Duration: 23 Jun 202026 Jun 2020

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume164
ISSN (Print)1868-8969

Conference

Conference36th International Symposium on Computational Geometry, SoCG 2020
Country/TerritorySwitzerland
CityZurich
Period23/06/2026/06/20

Keywords

  • Algebraic Topology
  • Inverse Problems
  • Persistent Homology
  • Spectral Geometry
  • Topological Data Analysis
  • Topological Transforms

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