Supervised distance metric learning through maximization of the Jeffrey divergence

作者：

Highlights：

• We propose a novel distance metric learning method (DMLMJ) for classification.

• DMLMJ is simple to implement and it can be solved analytically.

• We extend DMLMJ into a kernelized version to tackle non-linear problems.

• Experiments on several data sets show the effectiveness of the proposed method.

摘要

Highlights•We propose a novel distance metric learning method (DMLMJ) for classification.•DMLMJ is simple to implement and it can be solved analytically.•We extend DMLMJ into a kernelized version to tackle non-linear problems.•Experiments on several data sets show the effectiveness of the proposed method.

论文关键词：Distance metric learning,Nearest neighbor,Linear transformation,Jeffrey divergence

论文评审过程：Received 20 May 2016, Revised 24 August 2016, Accepted 13 November 2016, Available online 16 November 2016, Version of Record 27 November 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.11.010