Regularized coplanar discriminant analysis for dimensionality reduction

作者：

Highlights：

• RCDA simultaneously finds a projection matrix and linear representation coefficients.

• RCDA makes the samples from the same class coplanar.

• The linear representation coefficients are regularized by the proposed mean L2 norm.

• An optimization algorithm is proposed to solve the model of RCDA.

摘要

Highlights•RCDA simultaneously finds a projection matrix and linear representation coefficients.•RCDA makes the samples from the same class coplanar.•The linear representation coefficients are regularized by the proposed mean L2 norm.•An optimization algorithm is proposed to solve the model of RCDA.

论文关键词：Dimensionality reduction,Sparse representation classifier,Face recognition,Hyperspectral image classification,Coplanar discriminant analysis

论文评审过程：Received 13 January 2016, Revised 26 May 2016, Accepted 22 August 2016, Available online 26 August 2016, Version of Record 7 September 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.08.024