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• W2012508386 abstract "Sparse manifold learning has drawn more and more attentions recently, and sparsity preserving projections (SPP) has been proposed, which inherits the advantages of sparse reconstruction. However, SPP only focuses on the sparse structure, ignoring the discriminant information of labeled samples. In this paper, we proposed a new supervised dimensionality reduction method, which is called sparse discriminant embedding (SDE), for hyperspectral image (HSI) classification. SDE utilizes the merits of both intermanifold structure and sparsity property. It not only preserves the sparse reconstructive relations through l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -graph but also enhances the intermanifold separability of data, and the discriminating power of SDE is further improved than SPP. Experiments on two real HSIs collected by the Airborne Visible/Infrared Imaging Spectrometer and Reflective Optics System Imaging Spectrometer sensors are performed to demonstrate the effectiveness of the proposed SDE method." @default.
• W2012508386 created "2016-06-24" @default.
• W2012508386 creator A5062984211 @default.
• W2012508386 creator A5068963258 @default.
• W2012508386 date "2015-09-01" @default.
• W2012508386 modified "2023-10-15" @default.
• W2012508386 title "Dimensionality Reduction of Hyperspectral Images With Sparse Discriminant Embedding" @default.
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• W2012508386 doi "https://doi.org/10.1109/tgrs.2015.2418203" @default.
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