Efficient projection for compressed sensing

Proceedings - 7th IEEE/ACIS International Conference on Computer and Information Science, IEEE/ACIS ICIS 2008, In conjunction with 2nd IEEE/ACIS Int. Workshop on e-Activity, IEEE/ACIS IWEA 2008

Volumn , Issue , 2008, Pages 322-327

  Nhat, Vo Dinh Minh ^a   Vo, Duc ^b   Challa, Subhash ^b   Lee, SungYoung ^c  

^a Kyung Hee University   (South Korea)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSED SENSING; PROJECTION MATRICES;

ATOMIC PHYSICS; ATOMS; BOOLEAN FUNCTIONS; COMMUNICATION; COMPUTER NETWORKS; CYBERNETICS; INFORMATION MANAGEMENT; INFORMATION SCIENCE; MATRIX ALGEBRA; OPTIMIZATION; SIGNAL RECONSTRUCTION;

SINGULAR VALUE DECOMPOSITION;

EID: 51349086420     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIS.2008.72     Document Type: Conference Paper

References (19)

1. E. J. Candès and J. Romberg. Quantitative robust uncertainty principles and optimally sparse decompositions. Found. Comput. Math., 6(2):227-254, 2006.
2. E. J. Candes, J. K. Romberg, and T. Tao. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2):489-509, 2006.
3. E. J. Candes and T. Tao. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory, 52(12):5406-5425, 2006.
4. S. S. Chen, D. L. Donoho, and M. A. Saunders. Atomic decomposition by basis pursuit. SIAM J. Sci. Comput., 20(1):33-61, 1998.
5. J. A. Costa, N. Patwari, and I. Alfred O. Hero. Distributed weighted-multidimensional scaling for node localization in sensor networks. ACM Trans. Sen. Netw., 2(1):39-64, 2006.
6. T. F. Cox and M. A. A. Cox. Multidimensional Scaling. Chapman Hall, 1994.
7. D. Donoho and M. Elad. Optimally sparse representation in general (nonorthogonal) dictionaries via 11 minimization. Proc. Natl. Acad. Sci., 100:2197-2202, 2002.
8. D. L. Donoho. Compressed sensing. IEEE Transactions on Information Theory, 52(4):1289-1306, 2006.
9. M. Elad. Optimized projections for compressed sensing. IEEE Trans. on Signal Processing, 2007.
10. R. Gribonval and M. Nielsen. Sparse representations in unions of bases. IEEE Transactions on Information Theory, 49(12):3320-3325, 2003.
11. P. S. Huggins and S. W. Zucker. Greedy basis pursuit. IEEE Transactions on Signal Processing, 55(7):3760-3772, 2007.
12. A. Kearsley, R. Tapia, and M. Trosset. The Solution of the Metric STRESS and SSTRESS problems in Muldimensional Scaling Using Newton's Method. Computational Statistics, 13(3):369-396, 1998.
13. Lustig. Sparse mri: The application of compressed sensing for rapid mr imaging. 2007.
14. S. Mallat and Z. Zhang. Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12):3397-3415, 1993.
15. Y. Pad, R. Rezaiifar, and P. Krishnaprasad. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. Proceedings of the 27 th Annual Asilomar Conference on Signals, Systems, November 1993.
16. B. Richard. A lecture on compressive sensing. IEEE Signal Processing Magazine, July 2007.
17. M. A. Sheikh, O. Milenkovic, S. Sarvotham, and R. G. Baraniuk. Technical report, Rice University, 2007.
18. Y. Tsaig and D. L. Donoho. Extensions of compressed sensing. Signal Processing, 86(3):549-571, 2006.
19. M. B. Wakin, J. N. Laska, M. F. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. F. Kelly, and R. G. Baraniuk. An architecture for compressive imaging. In ICIP, pages 1273-1276, 2006.