On the Bootstrap for Persistence Diagrams and Landscapes
Algebraic Topology
2014-01-23 v2 Computational Geometry
Applications
Abstract
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 confidence bands for persistence landscapes.
Cite
@article{arxiv.1311.0376,
title = {On the Bootstrap for Persistence Diagrams and Landscapes},
author = {Frédéric Chazal and Brittany Terese Fasy and Fabrizio Lecci and Alessandro Rinaldo and Aarti Singh and Larry Wasserman},
journal= {arXiv preprint arXiv:1311.0376},
year = {2014}
}