Regularization feature selection projection twin support vector machine via exterior penalty

In the past years, non-parallel plane classifiers that seek projection direction instead of hyperplane for each class have attracted much attention, such as the multi-weight vector projection support vector machine (MVSVM) and the projection twin support vector machine (PTSVM). Instead of solving two generalized eigenvalue problems in MVSVM, PTSVM solves two related SVM-type problems to obtain the two projection directions by solving two smaller quadratic programming problems, similar to twin support vector machine. In order to suppress input space features, we propose a novel non-parallel classifier to automatically select significant features, called regularization feature selection projection twin support vector machine (RFSPTSVM). In contrast to the PTSVM, we first incorporate a regularization term to ensure the optimization problems are convex, and then replace all the terms with L₁-norm ones. By minimizing an exterior penalty function of the linear programming problem and using a fast generalized Newton algorithm, our RFSPTSVM obtains very sparse solutions. For nonlinear case, the method utilizes minimal number of kernel functions. The experimental results on toy datasets, Myeloma dataset, several UCI benchmark datasets, and NDCC generated datasets show the feasibility and effectiveness of the proposed method.