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Robust Lp-norm least squares support vector regression with feature selection

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  • Ye, Ya-Fen
  • Shao, Yuan-Hai
  • Deng, Nai-Yang
  • Li, Chun-Na
  • Hua, Xiang-Yu

Abstract

In this paper, we aim a novel algorithm called robust Lp-norm least squares support vector regression (Lp-LSSVR) that is more robust than the traditional least squares support vector regression(LS-SVR). Using the absolute constraint and the Lp-norm regularization term, our Lp-LSSVR performs robust against outliers. Moreover, though the optimization problem is non-convex, the sparse solution of Lp-norm and the lower bonds for nonzero components technique ensure useful features selected by Lp-LSSVR, and it helps to find the local optimum of our Lp-LSSVR. Experimental results show that although Lp-LSSVR is more robust than least squares support vector regression (LS-SVR), and much faster than Lp-norm support vector regression (Lp-SVR) and SVR due to its equality constraint, it is slower than LS-SVR and L1-norm support vector regression (L1-SVR), it is as effective as Lp-SVR, L1-SVR, LS-SVR and SVR in both feature selection and regression.

Suggested Citation

  • Ye, Ya-Fen & Shao, Yuan-Hai & Deng, Nai-Yang & Li, Chun-Na & Hua, Xiang-Yu, 2017. "Robust Lp-norm least squares support vector regression with feature selection," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 32-52.
    • Handle: RePEc:eee:apmaco:v:305:y:2017:i:c:p:32-52
    DOI: 10.1016/j.amc.2017.01.062
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    References listed on IDEAS

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    Cited by:

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    2. Yao Dong & He Jiang, 2018. "A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model," Complexity, Hindawi, vol. 2018, pages 1-12, November.
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    4. Kar Hoou Hui & Ching Sheng Ooi & Meng Hee Lim & Mohd Salman Leong & Salah Mahdi Al-Obaidi, 2017. "An improved wrapper-based feature selection method for machinery fault diagnosis," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-10, December.

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