Topological data analysis and machine learning

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...

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Veröffentlicht in:Advances in physics: X 2023-12, Vol.8 (1)
Hauptverfasser: Leykam, Daniel, Angelakis, Dimitris G.
Format: Artikel
Sprache:eng
Schlagworte:
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
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Zusammenfassung: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.
ISSN:2374-6149
2374-6149
DOI:10.1080/23746149.2023.2202331