The generalized eigenvalue proximal support vector machine (GEPSVM) and twin support vector machine (TWSVM) was proposed by Mangasarian and Jayadeva respectively, which aroused the interest of academia for its less computation cost and better generalization ability. They use the nonparallel hyperplane classifiers to solve the classification problem. Different from traditionally local or global TWSVM methods, a new Twin SVM algorithm called Local and Global Regularized Twin SVM (TWSVMLG) is proposed in this paper. A global regularizer was imposed across local models to smooth the data labels predicted by those local classifiers and avoid overfitting risk for the local classifiers. The classifier could get stronger discriminating ability when exploring local and global information than traditional algorithms. Finally some experimental results are presented to show the effectiveness of our algorithm.
