L-P Metrics on Multiparameter Persistence Modules
Offered By: Applied Algebraic Topology Network via YouTube
Course Description
Overview
Explore a comprehensive lecture on l_p-metrics for multiparameter persistence modules in topological data analysis. Delve into the generalization of p-Wasserstein distance on barcodes to multi-parameter persistence modules, introducing two key generalizations: d_I^p and d_M^p. Examine how these distances relate to the interleaving and matching distances, and discover their valuable properties. Investigate the continuity of 2-parameter multicover persistent homology, uncovering nuances in the stability theory not captured by the interleaving distance alone. Learn about the collaborative work with Havard Bjerkevik and gain insights from this talk, which was part of the "Topological Data Analysis - Theory and Applications" workshop supported by the Tutte Institute and Western University.
Syllabus
Michael Lesnick (5/3/21): l_p-Metrics on Multiparameter Persistence Modules
Taught by
Applied Algebraic Topology Network
Related Courses
Topological Data Analysis - New Perspectives on Machine Learning - by Jesse JohnsonOpen Data Science via YouTube Analyzing Point Processes Using Topological Data Analysis
Applied Algebraic Topology Network via YouTube MD Simulations and Machine Learning to Quantify Interfacial Hydrophobicity
Applied Algebraic Topology Network via YouTube Topological Data Analysis of Plant-Pollinator Resource Complexes - Melinda Kleczynski
Applied Algebraic Topology Network via YouTube Hubert Wagner - Topological Data Analysis in Non-Euclidean Spaces
Applied Algebraic Topology Network via YouTube