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ARC Colloquium: Brittany Terese Fasy–Tulane University
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(Note: location changed to CCB 102)
Title: Providing Statistical Guarantees for Topological Summaries of Data
Abstract:
Persistent homology is a method for probing topological properties of point clouds and function. The method involves tracking the birth and death of topological features as one varies a tuning parameter. Features with short lifetimes are informally considered to be “topological noise.” I am interested in bringing statistical ideas to persistent homology in order to distinguish topological signal from topological noise and to derive meaningful, yet computable, summaries of large datasets. In this talk, I will define some of the existing topological summaries of data, and show how we can provide statistical guarantees of these summaries.
www.fasy.us
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- Workflow status: Published
- Created by: Dani Denton
- Created: 11/25/2014
- Modified By: Fletcher Moore
- Modified: 04/13/2017
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