Exploiting Data Geometry in Machine Learning

Abstract

A key challenge in Machine Learning (ML) is the identification of geometric structure in high-dimensional data. Most algorithms assume that data lives in a high-dimensional vector space; however, many applications involve non-Euclidean data, such as graphs, strings and matrices, or data whose structure is determined by symmetries in the underlying system. Here, we discuss methods for identifying geometric structure in data and how leveraging data geometry can give rise to efficient ML algorithms with provable guarantees.