Robust one-class support vector machine with rescaled hinge loss function. Xing, H. & Ji, M. Pattern Recognition, 84:152–164, December, 2018. ![Robust one-class support vector machine with rescaled hinge loss function [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{xing_robust_2018,
title = {Robust one-class support vector machine with rescaled hinge loss function},
volume = {84},
issn = {00313203},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0031320318302498},
doi = {10.1016/j.patcog.2018.07.015},
abstract = {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.},
language = {en},
urldate = {2023-10-21},
journal = {Pattern Recognition},
author = {Xing, Hong-Jie and Ji, Man},
month = dec,
year = {2018},
pages = {152--164},
}
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