Support vector machine model with discriminant graph regularization term

Traditional SVM classification model constructs linear discriminant function by maximizing the margin between two classes, and the weight vector of the discriminant function is only related to a small number of support vectors near the decision boundary. The small amount of support vectors is hard to describe the global distributive information when the distributions of the samples are nonlinear manifolds structure. To solve this problem, the graph regularization term with discrimination information is introduced into the objective function of SVM model. Experimental results on public data sets show that the classification accuracy of this method has improved significantly compared to traditional SVM models.