Robust support vector machines based on the rescaled hinge loss function----Institute of Automation

Robust support vector machines based on the rescaled hinge loss function

Jan 06, 2017Author：

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Title:

Robust support vector machines based on the rescaled hinge loss function

Authors: Xu, GB; Cao, Z; Hu, BG; Principe, JC

Author Full Names: Xu, Guibiao; Cao, Zheng; Hu, Bao-Gang; Principe, Jose C.

Source:

PATTERN RECOGNITION, 63 139-148; 10.1016/j.patcog.2016.09.045

MAR 2017

Language: English

Abstract: The support vector machine (SVM) is a popular classifier in machine learning, but it is not robust to outliers. In this paper, based on the Correntropy induced loss function, we propose the resealed hinge loss function which is a monotonic, bounded and nonconvex loss that is robust to outliers. We further show that the hinge loss is a special case of the proposed resealed hinge loss. Then, we develop a new robust SVM based on the resealed hinge loss. After using the half-quadratic optimization method, we find that the new robust SVM is equivalent to an iterative weighted SVM, which can help explain the robustness of iterative weighted SVM from a loss function perspective. Experimental results confirm that the new robust SVM not only performs better than SVM and the existing robust SVMs on the datasets that have outliers, but also presents better sparseness than SVM.

ISSN: 0031-3203

eISSN: 1873-5142

IDS Number:

EE7HG

Unique ID: WOS:000389785900011

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