期刊
PATTERN RECOGNITION
卷 84, 期 -, 页码 152-164出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2018.07.015
关键词
One-class classification; One-class support vector machine; Hinge loss function; Half-quadratic optimization
资金
- National Natural Science Foundation of China [61672205, 61473111]
- Natural Science Foundation of Hebei province [F2017201020]
In this paper, a novel robust one-class support vector machine (OCSVM) based on the rescaled hinge loss function is proposed to enhance the robustness of the conventional OCSVM against outliers. The optimization problem of the proposed robust OCSVM can be iteratively solved by the half-quadratic optimization technique. Compared to OCSVM, robust OCSVM may achieve higher generalization performance from the theoretical analysis. Moreover, the robustness of robust OCSVM against outliers is explained from the weighted viewpoint. Experimental results on the synthetic and benchmark data sets demonstrate that the proposed robust OCSVM is superior to the conventional OCSVM and the other two related approaches. (C) 2018 Elsevier Ltd. All rights reserved.
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