Discriminative low-Rank embedding with manifold constraint for image feature extraction and classification
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Abstract:
Robustness to noise, outliers, and corruption is an important issue in image feature extraction. A discriminative low-rank embedded image feature extraction algorithm is proposed in this paper to address this problem. Firstly, manifold con-straints are introduced based on the low-rank embedding (LRE) approach to capture the geometric structure of the local manifold, taking into account both local and global information. Secondly, a discriminant analysis term is also introduced to obtain global discriminant information and learn the optimal projection matrix for data dimensionality reduction. Finally, the test samples are projected into a low-dimensional space for classification. Numerical experiments show that the classi-fication accuracy of the method proposed in this paper is 95.62%, 95.22%, 86.38%, and 86.54% on the ORL, CMUPIE, AR, and COIL20 datasets, respectively, which is better than that of the dimensionality reduction-based image feature ex-traction algorithm.
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This work has been supported by the National Natural Science Foundation of China (No. 61961037); Gansu Provincial Department of Education 2021 Industry Support Program (No. 2021CYZC-30).
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