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- W4308459032 abstract "Sparse representation-based classification (SRC) has attracted much attention by casting the recognition problem as simple linear regression problem. SRC methods, however, still is limited to enough labeled samples per category, insufficient use of unlabeled samples, and instability of representation. For tackling these problems, an unlabeled data driven inverse projection pseudo-full-space representation-based classification model is proposed with low-rank sparse constraints. The proposed model aims to mine the hidden semantic information and intrinsic structure information of all available data, which is suitable for few labeled samples and proportion imbalance between labeled samples and unlabeled samples problems in frontal face recognition. The mixed Gauss–Seidel and Jacobian ADMM algorithm is introduced to solve the model. The convergence, representation capability and stability of the model are analyzed. Experiments on three public datasets show that the proposed LR-S-PFSRC model achieves stable results, especially for proportion imbalance of samples. • An inverse projection-based SRC model is proposed for small sample and imbalanced problem in face recognition. • The model aims to mine the hidden semantic information and intrinsic structure information of all available data, especially unlabeled data. • The model is solved by a mixed Gauss–Seidel and Jacobian ADMM algorithm. • The convergence, representation capability and stability are analyzed." @default.
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- W4308459032 date "2023-02-01" @default.
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- W4308459032 title "Stable and compact face recognition via unlabeled data driven sparse representation-based classification" @default.
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- W4308459032 doi "https://doi.org/10.1016/j.image.2022.116889" @default.
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