Machine Learning with Topological Data Analysis features

Abstract: In Topological Data Analysis, Persistent Homology has been widely used to extract features from data. Such features are then used for clustering, visualization and classification. In this talk I will describe how we define Lipschitz continuous persistence features starting from pseudo metrics to compare topological representations of data. Special emphasis will be on the variety of different features that can be constructed in this way and how they can be used in machine learning pipelines. Joint work with the TDA group at KTH.

machine learningmathematical physicscommutative algebraalgebraic geometryalgebraic topologycombinatoricsdifferential geometrynumber theoryrepresentation theory

Audience: researchers in the topic

Machine Learning Seminar

Series comments: Online machine learning in pure mathematics seminar, typically held on Wednesday. This seminar takes place online via Zoom.

For recordings of past talks and copies of the speaker's slides, please visit the seminar homepage at: kasprzyk.work/seminars/ml.html