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• W2602222382 abstract "Subspace learning plays a key role in pattern recognition and machine learning. However, its performance would be degraded when data are corrupted by various occlusions. Low-rank representation (LRR) can recover the corrupted data and explore low-dimensional subspace structures embedded in data. Inspired by low-rank representation and subspace learning, in this paper, we propose a regularized low-rank representation model, which not only learns a low-rank matrix that can capture the global structure of data, but also derives a robust subspace from the corrupted data. The problem can be formulated a rank minimization objective function, which is effectively solved by numerical algorithm. The extensive experimental results on two facial expression databases demonstrate the effectiveness of the proposed method." @default.
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• W2602222382 date "2016-11-01" @default.
• W2602222382 modified "2023-09-27" @default.
• W2602222382 title "A regularized low-rank representation model for facial expression recognition" @default.
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• W2602222382 doi "https://doi.org/10.1109/icsp.2016.7877994" @default.
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