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.

与传统的一类支持向量机（OCSVM）相比，最小二乘OCSVM（LS-OCSVM）可以更准确地描述新样本和训练集之间的相似性。但是，LS-OCSVM对训练集中的异常值非常敏感。主要原因是离群值的平方误差函数的值相对较大，这使得LS-OCSVM更加重视这些离群值。为了提高LS-OCSVM对离群值的鲁棒性，提出了一种基于熵损失函数的鲁棒LS-OCSVM。结果，LS-OCSVM的无界凸平方损失函数被有界非凸熵变损失函数所替代。综合和基准数据集的实验结果表明，与相关方法相比，健壮的LS-OCSVM具有更好的抗离群值和泛化能力。