Why Topology for Machine Learning and Knowledge Extraction?

被引：9
|
作者：
Ferri, Massimo [1 ,2 ]
机构：
[1] Univ Bologna, Dept Math, I-40126 Bologna, Italy
[2] Univ Bologna, ARCES, I-40125 Bologna, Italy
来源：
MACHINE LEARNING AND KNOWLEDGE EXTRACTION | 2019年 / 1卷 / 01期
关键词：
shape; geometry; topological data analysis; persistence; SIZE FUNCTIONS; PATTERN-RECOGNITION;
D O I：
10.3390/make1010006
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页码：115 / 120
页数：6
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