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Volumn , Issue , 2009, Pages 60-66

Sparse signal recovery with exponential-family noise

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSED SENSING; DESIGN MATRIX; GAUSSIAN NOISE; GENERALIZED LINEAR MODEL; LINEAR PROJECTIONS; NOISE DISTRIBUTION; NOISY MEASUREMENTS; REGRESSION PROBLEM; SPARSE SIGNAL RECONSTRUCTION; SPARSE SIGNALS; SUFFICIENT CONDITIONS;

EID: 77949642178     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ALLERTON.2009.5394837     Document Type: Conference Paper
Times cited : (16)

References (19)
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  • 4
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    • Candes, E.1
  • 5
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    • Quantitative robust uncertainty principles and optimally sparse decompositions
    • 227-254, April
    • E. Candes and J. Romberg. Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations of Comput. Math., 6(2):227-254, April 2006.
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    • Candes, E.1    Romberg, J.2
  • 6
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    • Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
    • February
    • E. Candes, J. Romberg, and T. Tao. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. on Information Theory, 52(2):489-509, February 2006.
    • (2006) IEEE Trans. on Information Theory , vol.52 , Issue.2 , pp. 489-509
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  • 8
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    • Prediction and interpretation of distributed neural activity with sparse models
    • M.K. Carroll, G.A.Cecchi, I. Rish, R. Garg, and A.R. Rao. Prediction and interpretation of distributed neural activity with sparse models. Neuroimage, (44(1)):112-22, 2009.
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    • Carroll, M.K.1    Cecchi, G.A.2    Rish, I.3    Garg, R.4    Rao, A.R.5
  • 9
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  • 10
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    • 33744552752 scopus 로고    scopus 로고
    • For most large underdetermined systems of linear equations, the minimal ell-1 norm near-solution approximates the sparsest near-solution
    • July
    • D. Donoho. For most large underdetermined systems of linear equations, the minimal ell-1 norm near-solution approximates the sparsest near-solution. Communications on Pure and Applied Mathematics, 59(7):907-934, July 2006.
    • (2006) Communications on Pure and Applied Mathematics , vol.59 , Issue.7 , pp. 907-934
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  • 12
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    • June
    • D. Donoho. For most large underdetermined systems of linear equations, the minimal ell-1 norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics, (59(6)):797-829, June 2006.
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    • E. Candes and J. Romberg and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8):1207-1223, August 2006.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.