A transductive least squares support vector machine with the difference convex algorithm

Abstract

Unlabeled examples are easier and less expensive to obtain than labeled exam-ples. Semisupervised approaches are used to utilize such examples in an effort to boost the predictive performance. This paper proposes a novel semisupervised classification method named transductive least squares support vector machine (TLS-SVM), which is based on the least squares support vector machine. The proposed method utilizes the difference convex algorithm to derive nonconvex minimization solutions for the TLS-SVM. A generalized cross validation method is also developed to choose the hyperparameters that affect the performance of the TLS-SVM. The experimental results confirm the successful performance of the proposed TLS-SVM.