Semi-Supervised Machine Learning: a Homological Approach

TL;DR

This paper introduces a novel semi-supervised machine learning approach leveraging persistent homology and symbolic computation to improve learning algorithms.

Contribution

It presents a new mathematical framework for semi-supervised learning based on topological data analysis techniques.

Findings

Demonstrates the application of persistent homology in semi-supervised learning

Provides a mathematical foundation for the new approach

Shows potential advantages over traditional methods

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.