Low-rank preserving embedding

作者：

Highlights：

• Using low-rank representation for dimension reduction (LRPE).

• LRPE retains global discriminative structure into reduced space.

• Recast related methods into a unified problem and then tackle it.

• LRPE is more effective and robust, as well as cheap computation.

摘要

•Using low-rank representation for dimension reduction (LRPE).•LRPE retains global discriminative structure into reduced space.•Recast related methods into a unified problem and then tackle it.•LRPE is more effective and robust, as well as cheap computation.

论文关键词：Low-rank representation,Dimensionality reduction,Discriminative feature learning,Reconstruction relationship preserving projections

论文评审过程：Received 12 October 2016, Revised 14 March 2017, Accepted 4 May 2017, Available online 5 May 2017, Version of Record 17 May 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.05.003