A Sparse and Low-Rank Near-Isometric Linear Embedding Method for Feature Extraction in Hyperspectral Imagery Classification.
IEEE Transactions on Geoscience and Remote Sensing(2017)
摘要
A sparse and low-rank near-isometric linear embedding (SLRNILE) method has been proposed to make dimensionality reduction and extract proper features for hyperspectral imagery (HSI) classification. The SLRNILE stands on the theory of the John-Lindenstrauss lemma, and tries to estimate a sparse and low-rank projection matrix that satisfies the restricted isometric property (RIP) condition on all se...
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关键词
Feature extraction,Manifolds,Principal component analysis,Sparse matrices,Learning systems,Hyperspectral imaging
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