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Randomized SVD methods in hyperspectral imaging

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Publication:693807
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DOI10.1155/2012/409357zbMath1253.68120OpenAlexW1964542748WikidataQ58908543 ScholiaQ58908543MaRDI QIDQ693807

Jiani Zhang, Qiang Zhang, Xiaofei Hu, Jennifer B. Erway, Robert J. Plemmons

Publication date: 11 December 2012

Published in: Journal of Electrical and Computer Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2012/409357

zbMATH Keywords

classificationcompressionreconstructiontarget detectionhyperspectral datarandomized singular value decomposition


Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Coding and information theory (compaction, compression, models of communication, encoding schemes, etc.) (aspects in computer science) (68P30)


Related Items

Literature survey on low rank approximation of matrices



Cites Work

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  • Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
  • A randomized algorithm for a tensor-based generalization of the singular value decomposition
  • Principal component analysis.
  • Compressive-Projection Principal Component Analysis
  • A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing
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