Minimum Manifold-based Within-Class Scatter Support Vector Machine

Although Support Vector Machine (SVM) is widely used in practice, it only takes the boundary information between classes into consideration while neglects the data distribution, which seriously limits the classification efficiency. In view of this, Minimum Class Variance Support Vector Machine (MCVSVM) is proposed by Zafeiriou. Compared with SVM, MCVSVM has better generalization ability because it takes both boundary information and distribution characteristics into consideration. While the above mentioned methods SVM and MCVSVM always neglect the local characteristics of each class. Based on the above analysis, this paper presents Minimum Manifold-based Within-Class Scatter Support Vector Machine (M²SVM), which not only focuses on boundary information and distribution characteristics, but also preserves the manifold structure of each class. By theory analysis, M²SVM is equivalent to SVM and MCVSVM in a certain condition. It is believed that compared with SVM and MCVSVM, M²SVM has the best generalization ability. Experiments on the man-made dataset and UCI datasets verify the effectiveness of the proposed method M²SVM.