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Volumn , Issue , 2007, Pages 961-965

Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting

Author keywords

[No Author keywords available]

Indexed keywords

HIGH-DIMENSIONAL; INTERNATIONAL SYMPOSIUM;

EID: 51649127785     PISSN: 21578101     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIT.2007.4557348     Document Type: Conference Paper
Times cited : (44)

References (20)
  • 1
    • 33745604236 scopus 로고    scopus 로고
    • Stable signal recovery from incomplete and inaccurate measurements
    • August
    • E. Candes, 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.
    • (2006) Communications on Pure and Applied Mathematics , vol.59 , Issue.8 , pp. 1207-1223
    • Candes, E.1    Romberg, J.2    Tao, T.3
  • 2
    • 29144439194 scopus 로고    scopus 로고
    • Decoding by linear programming
    • December
    • E. Candes and T. Tao. Decoding by linear programming. IEEE Trans. Info Theory, 51(12):4203-4215, December 2005.
    • (2005) IEEE Trans. Info Theory , vol.51 , Issue.12 , pp. 4203-4215
    • Candes, E.1    Tao, T.2
  • 3
    • 47849114121 scopus 로고    scopus 로고
    • The Dantzig selector: Statistical estimation when p is much larger than n
    • E. Candes and T. Tao. The Dantzig selector: Statistical estimation when p is much larger than n. Annals of Statistics, 2006.
    • (2006) Annals of Statistics
    • Candes, E.1    Tao, T.2
  • 6
    • 33645712892 scopus 로고    scopus 로고
    • Compressed sensing
    • April
    • D. Donoho. Compressed sensing. IEEE Trans. Info. Theory, 52(4): 1289-1306, April 2006.
    • (2006) IEEE Trans. Info. Theory , vol.52 , Issue.4 , pp. 1289-1306
    • Donoho, D.1
  • 7
    • 33744552752 scopus 로고    scopus 로고
    • 1-norm near-solution approximates the sparsest near-solution
    • July
    • 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
    • Donoho, D.1
  • 9
    • 84888063335 scopus 로고    scopus 로고
    • D. L. Donoho and J. M. Tanner. Counting faces of randomly-projected polytopes when the projection radically lowers dimension. Technical report, Stanford University, 2006. Submitted to Journal of the AMS.
    • D. L. Donoho and J. M. Tanner. Counting faces of randomly-projected polytopes when the projection radically lowers dimension. Technical report, Stanford University, 2006. Submitted to Journal of the AMS.
  • 10
    • 0036714214 scopus 로고    scopus 로고
    • A generalized uncertainty principle and sparse representation in pairs of bases
    • September
    • M. Elad and A. M. Bruckstein. A generalized uncertainty principle and sparse representation in pairs of bases. IEEE Trans. Info Theory, 48(9):2558-2567, September 2002.
    • (2002) IEEE Trans. Info Theory , vol.48 , Issue.9 , pp. 2558-2567
    • Elad, M.1    Bruckstein, A.M.2
  • 11
    • 51649100478 scopus 로고    scopus 로고
    • Error bounds on sparse approximation
    • April
    • A. K. Fletcher, S. Rangan, and V. K. Goyal. Error bounds on sparse approximation. In ICASSP, April 2007.
    • (2007) ICASSP
    • Fletcher, A.K.1    Rangan, S.2    Goyal, V.K.3
  • 13
    • 0034287154 scopus 로고    scopus 로고
    • Adaptive estimation of a quadratic functional by model selection
    • B. Laurent and R Massart. Adaptive estimation of a quadratic functional by model selection. Annals of Statistics, 28(5): 1303-1338, 1998.
    • (1998) Annals of Statistics , vol.28 , Issue.5 , pp. 1303-1338
    • Laurent, B.1    Massart, R.2
  • 14
    • 4644315643 scopus 로고    scopus 로고
    • D. M. Malioutov, M. Cetin, and A. S. Willsky. Optimal sparse representations in general overcomplete bases. In Int. Conf. on Acoustics, Speech, and Signal Processing, 2, pages 11-793-796, May 2004.
    • D. M. Malioutov, M. Cetin, and A. S. Willsky. Optimal sparse representations in general overcomplete bases. In Int. Conf. on Acoustics, Speech, and Signal Processing, volume 2, pages 11-793-796, May 2004.
  • 15
    • 33747163541 scopus 로고    scopus 로고
    • High-dimensional graphs and variable selection with the lasso
    • To appear
    • N. Meinshausen and R Buhlmann. High-dimensional graphs and variable selection with the lasso. Annals of Statistics, 2006. To appear.
    • (2006) Annals of Statistics
    • Meinshausen, N.1    Buhlmann, R.2
  • 17
    • 33645712308 scopus 로고    scopus 로고
    • Just relax: Convex programming methods for identifying sparse signals in noise
    • March
    • J. Tropp. Just relax: Convex programming methods for identifying sparse signals in noise. IEEE Trans. Info Theory, 52(3): 1030-1051, March 2006.
    • (2006) IEEE Trans. Info Theory , vol.52 , Issue.3 , pp. 1030-1051
    • Tropp, J.1
  • 18
    • 84888064921 scopus 로고    scopus 로고
    • M. J. Wainwright. Information-theoretic bounds for sparsity recovery in the high-dimensional and noisy setting. Technical Report 725, Department of Statistics, UC Berkeley, January 2006. Posted as arxiv:math.ST/0702301.
    • M. J. Wainwright. Information-theoretic bounds for sparsity recovery in the high-dimensional and noisy setting. Technical Report 725, Department of Statistics, UC Berkeley, January 2006. Posted as arxiv:math.ST/0702301.
  • 19
    • 84888021760 scopus 로고    scopus 로고
    • 1 -constrained quadratic programs. In Proc. Allerton Conference on Communication, Control and Computing, October 2006. Long version appeared as UC Berkeley Technical Report 709.
    • 1 -constrained quadratic programs. In Proc. Allerton Conference on Communication, Control and Computing, October 2006. Long version appeared as UC Berkeley Technical Report 709.
  • 20
    • 84888050329 scopus 로고    scopus 로고
    • R Zhao and B. Yu. Model selection with the lasso. Technical report, UC Berkeley, Department of Statistics, March 2006. Accepted to Journal of Machine Learning Research.
    • R Zhao and B. Yu. Model selection with the lasso. Technical report, UC Berkeley, Department of Statistics, March 2006. Accepted to Journal of Machine Learning Research.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.