Linear classifier design in the weight space

作者：

Highlights：

• We propose a linear classifier design method in the weight space based on the observation that a training sample represents a plane in the weight space.

• We proposed search algorithms to find the optimal subspace, though the optimal solution may not be guaranteed since the full search cannot be used.

• Experimental results show that the proposed classifier performed better than or equivalent to existing linear classifiers (linear SVM, LDA).

摘要

•We propose a linear classifier design method in the weight space based on the observation that a training sample represents a plane in the weight space.•We proposed search algorithms to find the optimal subspace, though the optimal solution may not be guaranteed since the full search cannot be used.•Experimental results show that the proposed classifier performed better than or equivalent to existing linear classifiers (linear SVM, LDA).

论文关键词：Interior point,Linear classifier,Optimal linear classifier,Pyramid subspace,Weight space

论文评审过程：Received 22 September 2017, Revised 31 October 2018, Accepted 17 November 2018, Available online 19 November 2018, Version of Record 24 November 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.024