Topological data analysis and machine learning

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 review of app...

Full description

Saved in:
Bibliographic Details
Published inAdvances in physics: X Vol. 8; no. 1
Main Authors Leykam, Daniel, Angelakis, Dimitris G.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 31.12.2023
Taylor & Francis Ltd
Taylor & Francis Group
Subjects
condensed matter physics
Data analysis
Linear algebra
Machine learning
persistent homology
phase transition
Phase transitions
Physicists
Physics
Quantum computing
strongly correlated quantum systems
topological phase
Topology
Online AccessGet full text
ISSN2374-6149
2374-6149
DOI10.1080/23746149.2023.2202331

Cover

More Information
Summary: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 review of applications of topological data analysis to physics and machine learning problems in physics including the unsupervised detection of phase transitions. We finish with a preview of anticipated directions for future research.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2374-6149
2374-6149
DOI:10.1080/23746149.2023.2202331