YoVDO

Multi-Parameter Persistent Homology is Practical

Offered By: Applied Algebraic Topology Network via YouTube

Tags

Topological Data Analysis Courses Algorithm Optimization Courses Applied Algebraic Topology Courses

Course Description

Overview

Explore the practical applications of multi-parameter persistent homology in topological data analysis through this comprehensive lecture. Delve into recent algorithmic advancements that address the computational challenges of processing large datasets in this field. Learn about two key developments: the optimization of Lesnick and Wright's algorithm for computing minimal presentations of persistence modules, and an asymptotically fast method for calculating the exact matching distance. Gain insights into how these innovations are bridging the gap between theoretical research and real-world applications, making multi-parameter persistent homology more accessible and practical for data analysis tasks.

Syllabus

Michael Kerber (12/08/2021): Multi-Parameter Persistent Homology is Practical


Taught by

Applied Algebraic Topology Network

Related Courses

Topological Data Analysis - New Perspectives on Machine Learning - by Jesse Johnson
Open 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