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• W2895909348 abstract "Signal processing on graph offers the ability to define relationships of high-dimensional data on graph. In this paper, an unsupervised feature extraction method using graph for hyperspectral imagery is proposed, which incorporates collaborative representation using <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$ell _2$</tex-math></inline-formula> -norm regularization with locality constrained property into graph construction, named collaboration-competition preserving graph embedding. First, an undirected and weighted graph is constructed to exploit the data structure. Then, a weight matrix of edge in graph is built by formulating the combined collaborative-competitive representation into a convex optimization problem. The constructed graph is expected to reveal local intrinsic manifold and global geometry information of hyperspectral data. The superiority of the proposed graph-based unsupervised feature extraction method, compared with other traditional and state-of-the-art methods, is demonstrated by verifying the classification accuracy on four typical hyperspectral datasets." @default.
• W2895909348 created "2018-10-26" @default.
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• W2895909348 date "2018-12-01" @default.
• W2895909348 modified "2023-10-16" @default.
• W2895909348 title "Unsupervised Feature Extraction for Hyperspectral Imagery Using Collaboration-Competition Graph" @default.
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• W2895909348 doi "https://doi.org/10.1109/jstsp.2018.2877474" @default.
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