Laplacian total margin support vector machine based on within-class scatter

Insufficient volume of supervised information is a major challenge for supervised learning. An effective method to handle this problem is semi-supervised learning, which can make full use of the geometric information embedded in unlabeled instances. In this paper, we present a novel laplacian total margin support vector machine based on within-class scatter (LapWCS-TSVM) method to deal with the semi-supervised binary classification problem. The proposed LapWCS-TSVM incorporates the total margin algorithm and the manifold regularization into WCS-SVM to help improve its performance. With the help of kernel trick, the proposed LapWCS-TSVM can be easily generalized to non-linear separable case and solved by the optimization programming of the traditional support vector machine. Experiments conducted on artificial datasets, UCI datasets and face recognition datasets show the validity of the newly proposed algorithm.