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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...
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