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.
@article{arxiv.2301.11658,
title = {Semi-Supervised Machine Learning: a Homological Approach},
author = {Adrián Inés and César Domínguez and Jónathan Heras and Gadea Mata and Julio Rubio},
journal= {arXiv preprint arXiv:2301.11658},
year = {2023}
}
Comments
In Proceedings of XVII Encuentro \'algebra computacional y aplicaciones (EACA 2022). arXiv admin note: text overlap with arXiv:2205.09617