Semi-Supervised Machine Learning: a Homological Approach
arXiv:2301.11658v11 citationsh-index: 20
Originality Synthesis-oriented
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This addresses the challenge of leveraging unlabeled data in machine learning, but appears incremental as it applies existing topological concepts to a known problem.
The paper tackles the problem of semi-supervised machine learning by introducing a new method based on persistent homology, using symbolic computation and computer algebra techniques.
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.