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arXiv · 2301.11658

Semi-Supervised Machine Learning: a Homological Approach

Adrián Inés·César Domínguez·Jónathan Heras·Gadea Mata·Julio Rubio
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Abstract

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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Adrián Inés, César Domínguez, Jónathan Heras, Gadea Mata, Julio Rubio. 2023-01-27. Semi-Supervised Machine Learning: a Homological Approach. https://arxiv.org/abs/2301.11658

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