Sparse and Low-Rank Graph for Discriminant Analysis of Hyperspectral Imagery

@article{Li2016SparseAL,
  title={Sparse and Low-Rank Graph for Discriminant Analysis of Hyperspectral Imagery},
  author={Wei Li and Jiabin Liu and Qian Du},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2016},
  volume={54},
  pages={4094-4105}
}
Recently, sparse graph-based discriminant analysis (SGDA) has been developed for the dimensionality reduction and classification of hyperspectral imagery. In SGDA, a graph is constructed by ℓ1-norm optimization based on available labeled samples. Different from traditional methods (e.g., k-nearest neighbor with Euclidean distance), weights in an ℓ1-graph derived via a sparse representation can automatically select more discriminative neighbors in the feature space. However, the sparsity-based… CONTINUE READING
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