Face recognition by sparse discriminant analysis via joint L2,1-norm minimization

作者：

Highlights：

• Two novel Fisher Linear Discriminant Analysis (FLDA) methods based on L2,1-norm penalty are proposed.

• A modified Sparse Discriminant Analysis (SDA) based on L2,1-norm regularization is presented for jointly sparse feature extraction.

• L2,1-norm penalty on FLDA and SDA can significantly improve the recognition performance of FLDA and SDA, respectively.

摘要

Highlights•Two novel Fisher Linear Discriminant Analysis (FLDA) methods based on L2,1-norm penalty are proposed.•A modified Sparse Discriminant Analysis (SDA) based on L2,1-norm regularization is presented for jointly sparse feature extraction.•L2,1-norm penalty on FLDA and SDA can significantly improve the recognition performance of FLDA and SDA, respectively.

论文关键词：L2,1-norm,Fisher linear discriminant analysis,Sparse discriminant analysis

论文评审过程：Received 3 August 2013, Revised 3 January 2014, Accepted 15 January 2014, Available online 28 January 2014.

论文官网地址：https://doi.org/10.1016/j.patcog.2014.01.007