Adaptive feature selection via a new version of support vector machine

This paper focuses on feature selection in classification. A new version of support vector machine (SVM) named p-norm support vector machine ( $p\in0,1]$ ) is proposed. Different from the standard SVM, the p-norm $(p\in0,1])$ of the normal vector of the decision plane is used which leads to more sparse solution. Our new model can not only select less features but also improve the classification accuracy by adjusting the parameter p. The numerical experiments results show that our p-norm SVM is more effective than some usual methods in feature selection.