# Semi-Supervised Machine Learning: a Homological Approach

arXiv: https://arxiv.org/abs/2301.11658 · HTML: https://mlsanity.com/paper/2301.11658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [A topological classifier to characterize brain states: When shape matters more than variance](https://mlsanity.com/paper/2303.04231/) (2023)

## Abstract (arXiv)

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.