Feature selection for linear SVM with provable guarantees
作者：
Highlights：
• We give two provably accurate feature-selection techniques for the linear SVM.
• Algorithms can be used in supervised or unsupervised setting.
• We prove margin is preserved to within ε-relative error in the full feature space.
• In unsupervised case, we provide worst-case guarantees of margin and radius of minimum enclosing ball.
• Extensive experiments demonstrate that our method is competitive and often better than prior art.
摘要
Highlights•We give two provably accurate feature-selection techniques for the linear SVM.•Algorithms can be used in supervised or unsupervised setting.•We prove margin is preserved to within ε-relative error in the full feature space.•In unsupervised case, we provide worst-case guarantees of margin and radius of minimum enclosing ball.•Extensive experiments demonstrate that our method is competitive and often better than prior art.
论文关键词：Feature Selection,Sampling,Linear SVM
论文评审过程：Received 7 November 2015, Revised 1 April 2016, Accepted 4 May 2016, Available online 22 May 2016, Version of Record 4 June 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.05.018
