YAN Chunman.Discriminative low-rank embedding with manifold constraint for image feature extraction and classification[J].Optoelectronics Letters,2024,20(5):299-306 Discriminative low-rank embedding with manifold constraint for image feature extraction and classification Author Name Affiliation YAN Chunman  School of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Lanzhou 730070, China  Abstract:       The robustness against noise, outliers, and corruption is a crucial issue in image feature extraction. To address this concern, this paper proposes a discriminative low-rank embedding image feature extraction algorithm. Firstly, to enhance the discriminative power of the extracted features, a discriminative term is introduced using label information, obtaining global discriminative information and learning an optimal projection matrix for data dimensionality reduction. Secondly, manifold constraints are incorporated, unifying low-rank embedding and manifold constraints into a single framework to capture the geometric structure of local manifolds while considering both local and global information. Finally, test samples are projected into a lower-dimensional space for classification. Experimental results demonstrate that the proposed method achieves classification accuracies of 95.62%, 95.22%, 86.38%, and 86.54% on the ORL, CMUPIE, AR, and COIL20 datasets, respectively, outperforming dimensionality reduction-based image feature extraction algorithms. Hits: 0 Download times: 0 View Full Text    Download reader