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Why Topology for Machine Learning and Knowledge Extraction?

by Massimo Ferri 1,2,†
1
Department of Mathematics, University of Bologna, 40126 Bologna, Italy
2
ARCES, University of Bologna, 40125 Bologna, Italy
Current address: Department of Mathematics, University of Bologna, 40126 Bologna, Italy.
Mach. Learn. Knowl. Extr. 2019, 1(1), 115-120; https://doi.org/10.3390/make1010006
Received: 10 March 2018 / Revised: 26 April 2018 / Accepted: 30 April 2018 / Published: 2 May 2018
(This article belongs to the Section Topology)
Data has shape, and shape is the domain of geometry and in particular of its “free” part, called topology. The aim of this paper is twofold. First, it provides a brief overview of applications of topology to machine learning and knowledge extraction, as well as the motivations thereof. Furthermore, this paper is aimed at promoting cross-talk between the theoretical and applied domains of topology and machine learning research. Such interactions can be beneficial for both the generation of novel theoretical tools and finding cutting-edge practical applications. View Full-Text
Keywords: shape; geometry; topological data analysis; persistence shape; geometry; topological data analysis; persistence
MDPI and ACS Style

Ferri, M. Why Topology for Machine Learning and Knowledge Extraction? Mach. Learn. Knowl. Extr. 2019, 1, 115-120.

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