Large-scale linear nonparallel support vector machine solver

Abstract

Twin support vector machines (TWSVMs), as the representative nonparallel hyperplane classifiers, have shown the effectiveness over standard SVMs from some aspects. However, they still have one serious defect restricting their further study and real applications: they have to compute and store the inverse matrices before training, it is intractable for many applications such as that data appear with a huge number of instances as well as features. This paper proposes a Linear Nonparallel Support Vector Machine, termed as L2-TWSVM, to deal with large-scale data based on an efficient solver – dual coordinate descent (DCD) method. Both theoretical analysis and experiments indicate that our method is not only suitable for large scale problems, but also has better generalization performance than linear TWSVMs and linear SVMs.