On the Bootstrap for Persistence Diagrams and Landscapes

On the Bootstrap for Persistence Diagrams and Landscapes

Show other versions (1)

Persistent homology probes topological properties from point clouds and functions. By looking at multiple scales simultaneously, one can record the births and deaths of topological features as the scale varies. In this paper we use a statistical technique, the empirical bootstrap, to separate topolo...

Full description

Bibliographic Details
Main Authors: F. Chazal, B.T. Fasy, F. Lecci, A. Rinaldo, A. Singh, L. Wasserman
Format: Article
Language:English
Published: Yaroslavl State University 2013-12-01
Series:Modelirovanie i Analiz Informacionnyh Sistem
Subjects:
persistent homology
bootstrap topological data analysis
Online Access:https://www.mais-journal.ru/jour/article/view/162
Description
Summary:Persistent homology probes topological properties from point clouds and functions. By looking at multiple scales simultaneously, one can record the births and deaths of topological features as the scale varies. In this paper we use a statistical technique, the empirical bootstrap, to separate topological signal from topological noise. In particular, we derive confidence sets for persistence diagrams and confi- dence bands for persistence landscapes.The article is published in the author’s wording.
ISSN:1818-1015
2313-5417

Similar Items