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Robust least squares one-class support vector machine
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DOI：10.1016/j.patrec.2020.09.005.png)
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Abstract
En 中文
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. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
One-class support vector machine
Least square one-class support vector machine
Correntropy
One-class classification
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