Henry Adams (6/2/20): From persistent homology to machine learning
Title: From persistent homology to machine learning
Abstract: I will give an overview of a variety of ways to turn persistent homology output into input for machine learning tasks, including a discussion of the stability and interpretability properties of these methods. Persistent homology is a reasonable summary not only of the global topology but also of the local geometry present in a dataset. When persistent homology is interpreted by humans, the perspective is that the most persistent features matter the most, but machine learning algorithms heavily use low-persistent features measuring local geometry.
Paper link 1: http://jmlr.org/papers/volume18/16-33...
Paper link 2: https://doi.org/10.3389/frai.2021.668302
Slides: https://www.math.colostate.edu/~adams...