Multiple kernel dimensionality reduction based on collaborative representation for set oriented image classification

作者：

Highlights：

• A theoretical framework of set oriented dimensionality reduction is proposed.

• A new criterion for set oriented feature extraction based on CR is proposed.

• An optimization framework based on race ratio maximization is developed.

• Extensive experiments are conducted to show the effectiveness of our method.

摘要

•A theoretical framework of set oriented dimensionality reduction is proposed.•A new criterion for set oriented feature extraction based on CR is proposed.•An optimization framework based on race ratio maximization is developed.•Extensive experiments are conducted to show the effectiveness of our method.

论文关键词：Image set classification,Large margin,Collaborative representation,Multiple kernel learning,Orthogonal discriminative projection

论文评审过程：Received 1 May 2018, Revised 6 June 2019, Accepted 26 June 2019, Available online 27 June 2019, Version of Record 13 July 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.062