Topological Data Analysis and Its Applications in Machine Learning

Abstract

The recent applications of topological data analysis in machine learning are reviewed in this paper. Simplicial and persistent homology were briefly explained and then two such applications were described. The first application is a topological study of the activation of neural networks, and the second application is a convergence result for the stochastic subgradient method for topological loss functions.