Dual subspace discriminative projection learning

作者：

Highlights：

• We propose a novel feature extraction algorithm called dual subspace discriminative projection learning (DSDPL) for multi-class image classification with low sample size training data.

• Our approach serves to decompose original high dimensional data, via learned projection matrices, into class-shared and class-specific subspaces.

• Comprehensive experimental analysis is performed across five publicly available databases for face, object and scene classifications.

• Our experimental results demonstrate the effectiveness of DSDPL over current benchmark subspace learning methods and deep learning models.

摘要

•We propose a novel feature extraction algorithm called dual subspace discriminative projection learning (DSDPL) for multi-class image classification with low sample size training data.•Our approach serves to decompose original high dimensional data, via learned projection matrices, into class-shared and class-specific subspaces.•Comprehensive experimental analysis is performed across five publicly available databases for face, object and scene classifications.•Our experimental results demonstrate the effectiveness of DSDPL over current benchmark subspace learning methods and deep learning models.

论文关键词：Pattern recognition,Feature extraction,Subspace learning,Image classification,Subspace discriminative projection

论文评审过程：Received 3 May 2019, Revised 25 June 2020, Accepted 7 August 2020, Available online 25 August 2020, Version of Record 1 October 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107581