Manifold Energy Projection Twin Support Vector Machine for Semi-Supervised Classification

Abstract

The Projection Twin Support Vector Machine (PTSVM) and its variant, the Least Squares PTSVM (LSPTSVM), have demonstrated significant effectiveness in supervised classification tasks due to their strong generalization capabilities. However, their reliance on fully labeled data limits their applicability in real-world scenarios, where obtaining complete labeled datasets is often challenging and expensive. To overcome this limitation, we propose a novel Manifold Energy Projection Twin SVM (MEPTSVM) model for semi-supervised learning, which extends PTSVMs by integrating manifold regularization and energy-based margins. Our proposed MEPTSVM offers the following advancements over traditional PTSVMs: Firstly, MEPTSVM incorporates manifold regularization to leverage unlabeled data, capturing the underlying geometric structure of the dataset. By integrating both labeled and unlabeled samples, the model derives a more generalizable classification boundary that accounts for the global data distribution. Secondly, instead of employing a fixed distance margin between classes, MEPTSVM introduces an energy-based margin for each projection. This adaptive approach better captures the discriminative characteristics of different classes, enhancing classification performance and improving generalization. Finally, the effectiveness and practicality of MEPTSVM are demonstrated through extensive experiments on both synthetic and real-world datasets. The results validate its superiority in leveraging unlabeled data to improve classification accuracy and robustness.

Keywords

• Support vector machines
• semi-supervised learning
• regularization
• energy-based margin
• classification