Within-class penalty based multi-class support vector machine

Support vector machine (SVM) is a widely used maximum margin classifier, but the classification performance is largely affected by outliers. In this paper, we propose a novel multi-class SVM method to reduce the influence of outliers on the classification performance. Our proposed method includes an efficient optimization model via considering the within-class scatter and an optimization way. Specifically, the method is based on one assumption that penalizing the within-class scatter can reduce the number of misclassified outliers near the decision boundary, because data points of each class could be compacted by the within-class penalty. Experiments on benchmark databases demonstrate the effectiveness of the assumption and the proposed method.