Low rank representation with adaptive distance penalty for semi-supervised subspace classification

@article{Fei2017LowRR,
  title={Low rank representation with adaptive distance penalty for semi-supervised subspace classification},
  author={Lunke Fei and Yong Xu and Xiaozhao Fang and Jian Xi Yang},
  journal={Pattern Recognition},
  year={2017},
  volume={67},
  pages={252-262}
}
The graph based Semi-supervised Subspace Learning (SSL) methods treat both labeled and unlabeled data as nodes in a graph, and then instantiate edges among these nodes by weighting the affinity between the corresponding pairs of samples. Constructing a good graph to discover the intrinsic structures of the data is critical for these SSL tasks such as subspace clustering and classification. The Low Rank Representation (LRR) is one of powerful subspace clustering methods, based on which a… CONTINUE READING
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