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Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image
2021
Remote Sensing
Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information. Existing hypergraph learning methods only construct the hypergraph by a fixed similarity matrix or are adaptively optimal in original feature space; they do not update the hypergraph in subspace-dimensionality. In addition, the clustering performance obtained by the existing k-means-based clustering methods is
doi:10.3390/rs13071372
fatcat:fn3o7hqiyvfhdbo6c7tv7iaks4