Work

Statistical Inference For Persistent Homology: Confidence Sets For Persistence Diagrams

Year: 2013

Type: preprint

Abstract: Persistent homology is a method for probing properties of point clouds and functions. The method involves tracking the birth and death of features (2000) as one varies a tuning parameter. Features with short lifetimes are informally considered to be topological noise, and those with a long lifetime are considered to be topological signal. In this paper, we bring some statistical ideas to persistent homology. In particular, we derive confidence sets that allow us to separate signal from noise.

Source: arXiv (Cornell University)

Authors: Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry Wasserman, Sivaraman Balakrishnan, Aarti Singh

Language: English

Topic: Topological and Geometric Data Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2797851727

Cites:

Cited by: 8

Related to: 10