Robust least squares support vector machine based on recursive outlier elimination

To achieve robust estimation for noisy data set, a recursive outlier elimination-based least squares support vector machine (ROELS-SVM) algorithm is proposed in this paper. In this algorithm, statistical information from the error variables of least squares support vector machine is recursively learned and a criterion derived from robust linear regression is employed for outlier elimination. Besides, decremental learning technique is implemented in the recursive training-eliminating stage, which ensures that the outliers are eliminated with low computational cost. The proposed algorithm is compared with re-weighted least squares support vector machine on multiple data sets and the results demonstrate the remarkably robust performance of the ROELS-SVM.