Topological data analysis and machine learning
Mesoscale and Nanoscale Physics
2023-07-26 v3 Optics
Quantum Physics
Abstract
Topological data analysis refers to approaches for systematically and reliably computing abstract ``shapes'' of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise yet (we hope) comprehensive review of applications of topological data analysis to physics and machine learning problems in physics including the detection of phase transitions. We finish with a preview of anticipated directions for future research.
Keywords
Cite
@article{arxiv.2206.15075,
title = {Topological data analysis and machine learning},
author = {Daniel Leykam and Dimitris G. Angelakis},
journal= {arXiv preprint arXiv:2206.15075},
year = {2023}
}
Comments
Invited review, 15 pages, 7 figures, 117 references