Support vector machine with manifold regularization and partially labeling privacy protection

Abstract

A novel support vector machine with manifold regularization and partially labeling privacy protection, termed as SVM-MR&PLPP, is proposed for semi-supervised learning (SSL) scenarios where only few labeled data and the class proportion of unlabeled data, due to privacy protection concerns, are available. It integrates manifold regularization and privacy protection regularization into the Laplacian support vector machine (LapSVM) to improve the classification accuracy. Privacy protection here refers to use only the class proportion of data. In order to circumvent the high computational burden of the matrix inversion operation involved in SVM-MR&PLPP, its scalable version called SSVM-MR&PLPP is further developed by introducing intermediate decision variables into the original regularization framework so that the computational burden of the corresponding transformed kernel in SSVM-MR&PLPP can be greatly reduced, making it highly scalable to large datasets. The experimental results on numerous datasets show the effectiveness of the proposed classifiers.