Low‐rank nonnegative sparse representation and local preservation‐based matrix regression for supervised image feature selection

• Main Authors: Xingyu Zhu, Xiuhong Chen
• Format: Article
• Language: English
• Published: Wiley 2021-11-01
• Series: IET Image Processing
• Online Access: https://doi.org/10.1049/ipr2.12281
• ISSN: 1751-9659; 1751-9667

Abstract Matrix regression has attracted much attention due to directly select some meaningful features from matrix data. However, most existing matrix regressions do not consider the global and local structure of the matrix data simultaneously. To this end, we propose a low‐rank nonnegative sparse representation and local preserving matrix regression (LNSRLP‐MR) model for image feature selection. Here, the loss function is defined by the left and right regression matrices. To capture the global structure and discriminative information of the training images and reduce the effect of heterogeneous data and noises, we impose the low‐rank constraint on the self‐representation error matrix and the nonnegative sparse constraint on the coefficient vector. The graph matrix can be learned adaptively through representation coefficients, so that accurate local structure information in samples can be revealed. Feature selection is performed by obtained row sparse transformation matrix. An optimization procedure and its performance are also present. Experimental results on several image datasets show that compared with the‐state‐of‐the‐art method, the average classification accuracy of the proposed method is improved by at least 1.2% and up to 3.3%. For images with noise or occlusion, the accuracy is improved significantly, up to 4%, which indicates that this method has strong robustness.