An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists

With the recent explosion in the amount, the variety, and the dimensionality of available data, identifying, extracting, and exploiting their underlying structure has become a problem of fundamental importance for data analysis and statistical learning. Topological data analysis ( tda ) is a recent...

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Bibliographic Details
Published in:Frontiers in artificial intelligence Vol. 4; p. 667963
Main Authors: Chazal, Frédéric, Michel, Bertrand
Format: Journal Article
Language:English
Published: Frontiers Media S.A 29.09.2021
Series:Front. Artif. Intell
Subjects:
Algebraic Topology
Artificial Intelligence
Computer Science
geometric inference
Machine Learning
Mathematics
statistic
Statistics
topological data analysis
topological inference
ISSN:2624-8212, 2624-8212
Online Access:Get full text
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Summary:With the recent explosion in the amount, the variety, and the dimensionality of available data, identifying, extracting, and exploiting their underlying structure has become a problem of fundamental importance for data analysis and statistical learning. Topological data analysis ( tda ) is a recent and fast-growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data. It proposes new well-founded mathematical theories and computational tools that can be used independently or in combination with other data analysis and statistical learning techniques. This article is a brief introduction, through a few selected topics, to basic fundamental and practical aspects of tda for nonexperts.
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Devendra Singh Dhami, Darmstadt University of Technology, Germany
Edited by: Sriraam Natarajan, The University of Texas at Dallas, United States
Reviewed by: Ajey Kumar, Symbiosis International (Deemed University), India
This article was submitted to Machine Learning and Artificial Intelligence, a section of the journal Frontiers in Artificial Intelligence
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2021.667963