Transductive polyhedral conic classifiers for machine learning applications

Abstract

• We introduce new transductive learning methods that use both labeled and unlabeled data samples for learning. • The proposed methods use the polyhedral conic classification function as opposed to the linear SVM formulation. • We use the concave-convex procedure and robust loss functions to solve the resulting optimization problems. • The experimental results show that the proposed methods typically out- perform other transductive learning methods. In this paper, we introduce novel methods called Transductive Polyhedral Conic Classifiers that use both labeled and unlabeled data for classification. The proposed methodology utilizes the concave-convex procedure to solve the resulting optimization problems as in the Robust Transductive Support Vector Machines (RTSVMs). However, unlike RTSVM that uses SVM formulation, our proposed methods use the polyhedral conic classifier formulation that returns tight and closed decision boundaries compared to SVM. We tested the proposed methods on various datasets and experimental results show that our proposed methods yield better results than the existing transductive learning classifiers.