Topology Applied to Machine Learning: From Global to Local

TL;DR

This paper explores the evolution of applied topology in machine learning, emphasizing the importance of both global and local geometric features captured by persistent homology, and surveys diverse applications and methods.

Contribution

It highlights the significance of short persistent homology bars alongside long ones, advocating for their combined use in machine learning tasks and providing a comprehensive survey of related applications.

Findings

Short bars are as important as long bars in many ML tasks.

Persistent homology techniques effectively incorporate local and global data features.

Survey covers applications across various scientific fields.

Abstract

Through the use of examples, we explain one way in which applied topology has evolved since the birth of persistent homology in the early 2000s. The first applications of topology to data emphasized the global shape of a dataset, such as the three-circle model for $3 \times 3$ pixel patches from natural images, or the configuration space of the cyclo-octane molecule, which is a sphere with a Klein bottle attached via two circles of singularity. In these studies of global shape, short persistent homology bars are disregarded as sampling noise. More recently, however, persistent homology has been used to address questions about the local geometry of data. For instance, how can local geometry be vectorized for use in machine learning problems? Persistent homology and its vectorization methods, including persistence landscapes and persistence images, provide popular techniques for incorporating both local geometry and global topology into machine learning. Our meta-hypothesis is that the short bars are as important as the long bars for many machine learning tasks. In defense of this claim, we survey applications of persistent homology to shape recognition, agent-based modeling, materials science, archaeology, and biology. Additionally, we survey work connecting persistent homology to geometric features of spaces, including curvature and fractal dimension, and various methods that have been used to incorporate persistent homology into machine learning.