Mixed-norm linear support vector machine
This paper presents a new version of support vector machine (SVM) named l 2?l p SVM (0<p<1) which introduces the l p -norm (0<p<1) of the normal vector of the decision plane in the standard linear SVM. To solve the nonconvex optimization problem in our model, an efficient algorithm is proposed using the constrained concaveconvex procedure. Experiments with artificial data and real data demonstrate that our method is more effective than some popular methods in selecting relevant features and improving classification accuracy.
support vector machine optimization constrained concaveconvex procedure norm feature selection