Persistent Homology and Machine Learning

Abstract

In this position paper, we present a brief overview of the ways topological tools, in particular persistent homology, has been applied to machine learning and data analysis problems. We provide an introduction to the area, including an explanation as to how topology may capture higher order information. We also provide numerous references for the interested reader and conclude with some current directions of research.

Authors

  • Primož Škraba Jozef Stefan Institute

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Published

03/27/2018

How to Cite

Škraba, P. (2018). Persistent Homology and Machine Learning. Informatica, 42(2). Retrieved from https://www.informatica.si/index.php/informatica/article/view/2236

Issue

Vol. 42 No. 2 (2018)

Section

Regular papers

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