Least Absolute Deviation Support Vector Regression

Kuaini Wang, Jingjing Zhang, Yanyan Chen and Ping Zhong

Mathematical Problems in Engineering, 2014, vol. 2014, 1-8

Abstract:

Least squares support vector machine (LS-SVM) is a powerful tool for pattern classification and regression estimation. However, LS-SVM is sensitive to large noises and outliers since it employs the squared loss function. To solve the problem, in this paper, we propose an absolute deviation loss function to reduce the effects of outliers and derive a robust regression model termed as least absolute deviation support vector regression (LAD-SVR). The proposed loss function is not differentiable. We approximate it by constructing a smooth function and develop a Newton algorithm to solve the robust model. Numerical experiments on both artificial datasets and benchmark datasets demonstrate the robustness and effectiveness of the proposed method.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:169575

DOI: 10.1155/2014/169575

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