A new semi-supervised PSVM classifier

Abstract

Based on the ideas of LIAM and the U-support vector machine, this paper proposes a new semi-supervised proximal support vector machine, which only requires solving the inverse of an n+1-by-n+1 matrix to obtain the final classification hyperplane just as the PLIAM and is faster and more efficient than other mathematical programming-based methods. The most essential is that this method overcomes the two fundamental drawbacks of the general LIAM semi-supervised support vector classifiers: (1) they included the whole information provided by both the positively and negatively labeled instances through the unlabeled instances that are in its neighborhood in the linear constraints, which greatly added the number of constraints and made the optimization solver more complex; (2) they can only utilize the unlabeled points that are in the neighborhood of a labeled point, which may influence the accuracy of classification. The experiments on public benchmarks indicate that our semi-supervised PSVM classifier is more accurate than the original PLIAM semi-supervised classification method.