HyperGraph-based Minimax Probability Machines for Semi-Supervised Learning

Reshma Rastogi, Sambhav Jain, Ankush Bisht,
HyperGraph-based Minimax Probability Machines for Semi-Supervised Learning,
Procedia Computer Science,
Volume 258,
2025,
Pages 3703-3712,
ISSN 1877-0509,
https://doi.org/10.1016/j.procs.2025.04.625.
(https://www.sciencedirect.com/science/article/pii/S1877050925017296)
Abstract: Semi-Supervised Learning (SSL) is a method that combines both supervised as well as unsupervised learning to effectively learn from the humongous unlabelled data. In this paper, we introduce a Semi-Supervised Minimax Probability Machine model based on the Hypergraph Laplacian, leveraging hypergraphs to capture higher-order correlations within the intrinsic geometry of the manifold, thereby enhancing the learning process. Extensive experimental evaluations on well-known multi-label datasets showcase the efficacy and validity of our proposed model.
Keywords: Minimax Probability Machines; Hypergraph Laplacian; Second Order Cone Programming Problem; weighted least squares