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In this paper we describe the mathematical foundations of a new approach to semi-supervised Machine Learning. Using techniques of Symbolic Computation and Computer Algebra, we apply the concept of persistent homology to obtain a new semi-supervised learning method.
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@article{ins2023semisupervised,
title = {Semi-Supervised Machine Learning: a Homological Approach},
author = {Adrián Inés and César Domínguez and Jónathan Heras and Gadea Mata and Julio Rubio},
year = {2023},
eprint = {2301.11658},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2301.11658v1}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2301.11658)