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In the real world, data samples are often contaminated. Using these contaminated data samples for subspace segmentation usually leads to segmentation results distorted. To remove error, many existing subspace segmentation algorithms directly use different regularization to model the error of the corresponding type in the objective. However, the priori of errors is difficult to obtain in practice,...
Low-rank representation (LRR) and its variations have recently attracted a great deal of attention because of its effectiveness in exploring low-dimensional subspace structures embedded in data. LRR-related algorithms have many applications in computer vision, signal processing, semi-supervised learning and pattern recognition. However, most of the existing LRR methods fail to take into account the...
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