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Variable selection for support vector machines in moderately high dimensions

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  • Xiang Zhang
  • Yichao Wu
  • Lan Wang
  • Runze Li

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  • Xiang Zhang & Yichao Wu & Lan Wang & Runze Li, 2016. "Variable selection for support vector machines in moderately high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 53-76, January.
    • Handle: RePEc:bla:jorssb:v:78:y:2016:i:1:p:53-76
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    References listed on IDEAS

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    1. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    2. Yongdai Kim & Sunghoon Kwon, 2012. "Global optimality of nonconvex penalized estimators," Biometrika, Biometrika Trust, vol. 99(2), pages 315-325.
    3. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    4. Bartlett, Peter L. & Jordan, Michael I. & McAuliffe, Jon D., 2006. "Convexity, Classification, and Risk Bounds," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 138-156, March.
    5. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    6. Le An & Pham Tao, 2005. "The DC (Difference of Convex Functions) Programming and DCA Revisited with DC Models of Real World Nonconvex Optimization Problems," Annals of Operations Research, Springer, vol. 133(1), pages 23-46, January.
    7. Kim, Yongdai & Choi, Hosik & Oh, Hee-Seok, 2008. "Smoothly Clipped Absolute Deviation on High Dimensions," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1665-1673.
    8. Mazumder, Rahul & Friedman, Jerome H. & Hastie, Trevor, 2011. "SparseNet: Coordinate Descent With Nonconvex Penalties," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1125-1138.
    9. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    10. Lan Wang & Yichao Wu & Runze Li, 2012. "Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 214-222, March.
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    Cited by:

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    2. Xin Liu & Bangxin Zhao & Wenqing He, 2020. "Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM," Mathematics, MDPI, vol. 8(10), pages 1-22, October.
    3. Zhang, Xiaochen & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2022. "Subgroup analysis for high-dimensional functional regression," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    4. Li-Pang Chen, 2022. "Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-25, September.
    5. Kim, Hyungwoo & Shin, Seung Jun, 2026. "Variable selection in AUC-optimizing classification," Computational Statistics & Data Analysis, Elsevier, vol. 213(C).
    6. Wang, Qin & Osei, Edmund, 2026. "Boosted sliced regression for dimension reduction in binary classification," Computational Statistics & Data Analysis, Elsevier, vol. 218(C).
    7. Yoosoon Chang & Joon Y. Park & Guo Yan, 2026. "Using SVM to Estimate and Predict Binary Choice Models," Papers 2601.22659, arXiv.org.
    8. Hongcheng Liu & Yinyu Ye & Hung Yi Lee, 2022. "High-Dimensional Learning Under Approximate Sparsity with Applications to Nonsmooth Estimation and Regularized Neural Networks," Operations Research, INFORMS, vol. 70(6), pages 3176-3197, November.
    9. Rachid Kharoubi & Abdallah Mkhadri & Karim Oualkacha, 2024. "High-dimensional penalized Bernstein support vector classifier," Computational Statistics, Springer, vol. 39(4), pages 1909-1936, June.
    10. Kangning Wang & Xiaoqing Meng & Xiaofei Sun, 2025. "Convolution smoothing and online updating estimation for support vector machine," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 34(1), pages 288-323, March.
    11. Jiang, He & Tao, Changqi & Dong, Yao & Xiong, Ren, 2021. "Robust low-rank multiple kernel learning with compound regularization," European Journal of Operational Research, Elsevier, vol. 295(2), pages 634-647.
    12. Xingcai Zhou & Hao Shen, 2022. "Communication-Efficient Distributed Learning for High-Dimensional Support Vector Machines," Mathematics, MDPI, vol. 10(7), pages 1-21, March.

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