Low-rank inter-class sparsity based semi-flexible target least squares regression for feature representation
作者：
Highlights：
• We proposed a novel LIS_StLSR method for robust image classification.
• LIS_StLSR consists of a low-rank inter-class sparsity constraint.
• A semi-flexible regression target is also proposed to measure the regression error.
• LIS_StLSR obtains low-dimensional and highly-discriminant feature representation.
• Proposed method achieves state-of-the-art performance in extensive experiments.
摘要
•We proposed a novel LIS_StLSR method for robust image classification.•LIS_StLSR consists of a low-rank inter-class sparsity constraint.•A semi-flexible regression target is also proposed to measure the regression error.•LIS_StLSR obtains low-dimensional and highly-discriminant feature representation.•Proposed method achieves state-of-the-art performance in extensive experiments.
论文关键词：Least squares regression,Low-rank inter-class sparsity,Feature representation,Image classification
论文评审过程：Received 6 January 2021, Revised 8 July 2021, Accepted 20 September 2021, Available online 5 October 2021, Version of Record 16 October 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108346
