Robust least squares one-class support vector machine

Abstract

Abstract In comparison with the conventional one-class support vector machine (OCSVM), least squares OCSVM (LS-OCSVM) can describe similarity between a new-coming sample and training set more accurately. However, LS-OCSVM is very sensitive to outliers in training set. The main reason lies that the values of square error function for outliers are relatively large, which makes LS-OCSVM put more emphasis on these outliers. To enhance the robustness of LS-OCSVM against outliers, a novel robust LS-OCSVM based on correntropy loss function is proposed. As a result, the unbounded convex square loss function of LS-OCSVM is substituted by a bounded nonconvex correntropy loss function. Experimental results on synthetic and benchmark data sets show that robust LS-OCSVM possesses better anti-outlier and generalization abilities in comparison with its related approaches.