Orthogonal matching pursuit with replacement

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Jain, Prateek ^a   Tewari, Ambuj ^b   Dhillon, Inderjit S ^b  

^a MICROSOFT RESEARCH INDIA   (India)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; MATRIX ALGEBRA;

COMPRESSED-SENSING; HARD THRESHOLDING; HARD THRESHOLDING OPERATORS; ITERATIVE ALGORITHM; LINEAR MEASUREMENTS; ORTHOGONAL MATCHING PURSUIT; SPARSE RECOVERY; SPARSE VECTORS; SPECTRA'S; THRESHOLDING ALGORITHMS;

RECOVERY;

EID: 85162325406     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. T. Blumensath and M. E. Davies. Iterative hard thresholding for compressed sensing. Applied and Computational Harmonic Analysis, 27(3):265-274,2009.
2. E. J. Candes. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 346(9-10):589-592,2008.
3. E. J. Candes and T. Tao. Decoding by linear programming. IEEE Transactions on Information Theory, 51(12):4203-4215,2005.
4. W. Dai andO. Milenkovic. Subspace pursuit for compressive sensing signal reconstruction. IEEE Transactions on Information Theory, 55(5):2230-2249,2009.
5. M. A. Davenport and M. B. Wakin. Analysis of orthogonal matching pursuit using the restricted isometry property. IEEE Transactions on Information Theory, 56(9):4395-4401,2010.
6. G. Davis, S. Mallat, and M. Avellaneda. Greedy adaptive approximation. Constr. Approx, 13:57-98,1997.
7. D. Donoho. Compressed sensing. IEEE Trans, on Information Theory, 52(4): 1289-1306,2006.
8. D. Donoho, A. Maleki, and A. Montanari. Message passing algorithms for compressed sensing. Proceedings of the National Academy of Sciences USA, 106(45):18914-18919,2009.
9. M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baranuik. Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 25(2):83-91, March 2008.
10. S. Foucart. Hard thresholding pursuit: an algorithm for compressive sensing, 2010. preprint.
11. 1- minimization. Applied and Computational Harmonic Analysis, 29(1):97-103,2010.
12. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions using hashing. In Proceedings of 25th International Conference on Very Large Data Bases, 1999.
13. M. X. Goemans and D. R Williamson. .879-approximation algorithms for MAX CUT and MAX 2SAT. In STOC, pages 422-431,1994.
14. D. Hsu, S. M. Kakade, J. Langford, and T. Zhang. Multi-label prediction via compressed sensing. In Advances in Neural Information Processing Systems, 2009.
15. R Jain, R. Meka, and I. S. Dhillon. Guaranteed rank minimization via singular value projection. In Advances in Neural Information Processing Systems, 2010.
16. A. Maleki. Convergence analysis of iterative thresholding algorithms. In Allerton Conference on Communication, Control and Computing, 2009.
17. A. Maleki and D. Donoho.Optimally tuned iterative reconstruction algorithms for compressed sensing. IEEE Journal of Selected Topics in Signal Processing, 4(2):330-341,2010.
18. Q. Mo and Y. Shen. Remarks on the restricted isometry property in orthogonal matching pursuit algorithm, 2011. preprint arXiv:1101.4458.
19. D. Needell and J. A. Tropp. Cosamp: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis, 26(3):301-321,2009.
20. S. Negahban, R Ravikumar, M. J. Wainwright, and B. Yu. A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers. In Advances in Neural Information Processing Systems, 2009.
21. Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad.Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In 27th Annu. Asilomar Conf. Signals, Systems, and Computers, volume 1, pages 40-44,1993.
22. H. Rauhut.On the impossibility of uniform sparse reconstruction using greedy methods. Sampling Theory in Signal and Image Processing, 7(2):197-215,2008.
23. S. Shalev-Shwartz, N. Srebro, and T. Zhang. Trading accuracy for sparsity in optimization problems with sparsity constraints. SI AM Journal onOptimization, 20:2807-2832,2010.
24. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 98(6):1031-1044,2010.
25. T. Zhang. Adaptive forward-backward greedy algorithm for sparse learning with linear models. In Advances in Neural Information Processing Systems, 2008.
26. T. Zhang. Sparse recovery with orthogonal matching pursuit under RIP, 2010. preprint arXiv: 1005.2249.