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Volumn 22, Issue , 2012, Pages 942-950

Universal measurement bounds for structured sparse signal recovery

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPRESSED SENSING; RECOVERY;

EID: 84937847125     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (31)

References (25)
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    • On Milman's inequality and random subspaces which escape through a mesh in Rn. Geometric aspects of functional analysis
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    • Technical report, arXiv: 0901.2962. Preprint, May
    • J. Huang and T Zhang. The benefit of group sparsity. Technical report, arXiv: 0901.2962. Preprint available at http://arxiv.org/pdf/0903.2962v2, May 2009.
    • (2009) The Benefit of Group Sparsity
    • Huang, J.1    Zhang, T.2
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    • Reconstruction and subgaussian operators in asymptotic geometric analysis
    • S. Mendelson, A. Pajor, and N. Tomczak-Jaegermann. Reconstruction and subgaussian operators in asymptotic geometric analysis. Geometric and Functional Analysis, 17 (4): 1248-1282, 2006.
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    • Mendelson, S.1    Pajor, A.2    Tomczak-Jaegermann, N.3
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    • Blind multi-band signal reconstruction: Compressed sensing for analog signals
    • March
    • M. Mishali and Y. Eldar. Blind multi-band signal reconstruction: compressed sensing for analog signals. IEEE Trans. Signal Processing, 57 (30): 993-1009, March 2009.
    • (2009) IEEE Trans. Signal Processing , vol.57 , Issue.30 , pp. 993-1009
    • Mishali, M.1    Eldar, Y.2
  • 17
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    • Cosamp: Iterative signal recovery from incomplete and inaccurate samples
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    • Bayesian tree structured image modeling using wavelet domain hidden markov models
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.