Robust Lp-norm least squares support vector regression with feature selection

Abstract

In this paper, we aim a novel algorithm called robust Lp-norm least squares support vector regression (Lp-LSSVR) that is more robust than the traditional least squares support vector regression(LS-SVR). Using the absolute constraint and the Lp-norm regularization term, our Lp-LSSVR performs robust against outliers. Moreover, though the optimization problem is non-convex, the sparse solution of Lp-norm and the lower bonds for nonzero components technique ensure useful features selected by Lp-LSSVR, and it helps to find the local optimum of our Lp-LSSVR. Experimental results show that although Lp-LSSVR is more robust than least squares support vector regression (LS-SVR), and much faster than Lp-norm support vector regression (Lp-SVR) and SVR due to its equality constraint, it is slower than LS-SVR and L1-norm support vector regression (L1-SVR), it is as effective as Lp-SVR, L1-SVR, LS-SVR and SVR in both feature selection and regression.