Journal
IEEE ACCESS
Volume 6, Issue -, Pages 20334-20347Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2822546
Keywords
Classification algorithms; Lp-norm; regularization; robustness; support vector machines
Categories
Funding
- National Natural Science Foundation of China [61703370, 61603338]
- Natural Science Foundation of Zhejiang Province [LQ17F030003, LY15F030013, LY18G010018]
- Natural Science Foundation of Hainan Province [118QN181]
- Scientific Research Fund of Zhejiang Provincial Education Department [Y201534889]
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As a useful classification method, generalized eigenvalue proximal support vector machine (GEPSVM) is recently studied extensively. However, it may suffer from the sensitivity to outliers, since the L2-norm is used as a measure distance. In this paper, based on the robustness of the L1-norm, we propose an improved robust L1-norm nonparallel proximal SVM with an arbitrary Lp-norm regularization (LpNPSVM), where p > 0. Compared with GEPSVM, the LpNPSVM is more robust to outliers and noise. A simple but effective iterative technique is introduced to solve the LpNPSVM, and its convergence guarantee is also given when 0 < p <= 2. Experimental results on different types of contaminated data sets show the effectiveness of LpNPSVM. At last, we investigate our LpNPSVM on a real spare parts inspection problem. Computational results demonstrate the effectiveness of the proposed method over the GEPSVM on all the noise data.
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