Convergence rate of SVM for kernel-based robust regression

摘要

It is known that to alleviate the performance deterioration caused by the outliers, the robust support vector (SV) regression is proposed, which is essentially a convex optimization problem associated with a non-convex loss function. The theory analysis for its performance cannot. be finished by the usual convex analysis approach. For a robust SV regression algorithm containing two homotopy parameters, a non-convex method is developed with the quasiconvex analysis theory and the error estimate is given. An explicit convergence rate is provided, and the effect degree of outliers on the performance is quantitatively shown.