1-Norm Projection Twin Support Vector Machine.

In this paper, we propose a novel feature selection method which can suppress the input features automatically. We first introduce a Tikhonov regularization term to the objective function of projection twin support vector machine (PTSVM). Then we convert it to a linear programming (LP) problem by replacing all the 2-norm terms in the objective function with 1-norm ones. Then we construct an unconstrained convex programming problem according to the exterior penalty (EP) theory. Finally, we solve the EP problems by using a fast generalized Newton algorithm. In order to improve performance, we apply a recursive algorithm to generate multiple projection axes for each class. To disclose the feasibility and effectiveness of our method, we conduct some experiments on UCI and Binary Alphadigits data sets.