Date of Original Version

1-22-2014

Type

Article

Abstract or Description

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.

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Published In

Modeling and Analysis of Information Systems, 20, 6, 111-120.