Topological data analysis and machine learning

IF 7.7 2区物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY

Advances in Physics: X Pub Date : 2022-06-30 DOI:10.1080/23746149.2023.2202331

D. Leykam, D. Angelakis

引用次数: 6

Abstract

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 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. Graphical abstract

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拓扑数据分析和机器学习

拓扑数据分析是指系统可靠地计算复杂数据集的抽象“形状”的方法。拓扑数据分析在生命科学和数据科学中有各种各样的应用，物理学家对此越来越感兴趣。我们简要回顾了拓扑数据分析在物理和物理中的机器学习问题中的应用，包括相变的无监督检测。最后，我们展望了未来研究的预期方向。图形抽象

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Physics: X Physics and Astronomy-General Physics and Astronomy

CiteScore

13.60

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Advances in Physics: X is a fully open-access journal that promotes the centrality of physics and physical measurement to modern science and technology. Advances in Physics: X aims to demonstrate the interconnectivity of physics, meaning the intellectual relationships that exist between one branch of physics and another, as well as the influence of physics across (hence the “X”) traditional boundaries into other disciplines including: Chemistry Materials Science Engineering Biology Medicine