Sequentially designed compressed sensing

2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Volumn , Issue , 2012, Pages 401-404

  Haupt, Jarvis ^a   Baraniuk, Richard ^b   Castro, Rui ^c   Nowak, Robert ^d  

^a University of Minnesota   (United States)

^b Rice University   (United States)

^c EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^d Wisconsin Electric Machines and Power Electronics Consortium   (United States)

Author keywords

Adaptive sensing; compressed sensing; support recovery

Indexed keywords

ADAPTIVE SENSING; COMPRESSIVE SENSING; SIGNAL COMPONENTS; SIGNAL SUPPORT; SPARSE SIGNALS;

RECOVERY;

SIGNAL RECONSTRUCTION;

EID: 84868218048     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSP.2012.6319715     Document Type: Conference Paper

References (15)

1. D. Donoho, "Compressed sensing," IEEE Trans. Inform. Theory, Vol. 52, no. 4, pp. 1289-1306, 2006.
2. E. Candes and T. Tao, "Near optimal signal recovery from random projections: Universal encoding strategies?," IEEE Trans. Inform. Theory, Vol. 52, no. 12, pp. 5406-5425, 2006. (Pubitemid 46445381)
3. 1-constrained quadratic programming (lasso)," IEEE Trans. Inform. Theory, Vol. 55, no. 5, pp. 2183-2202, 2009.
4. C. Genovese, J. Jin, and L. Wasserman, "Revisiting marginal regression," Manuscript, Nov. 2009.
5. S. Aeron, V. Saligrama, and M. Zhao, "Information theoretic bounds for compressed sensing," IEEE Trans. Inform. Theory, Vol. 56, no. 10, pp. 5111-5130, 2010.
6. J. Haupt, R. Castro, and R. Nowak, "Distilled sensing: Adaptive sampling for sparse detection and estimation," IEEE Trans. Inform. Theory, Vol. 57, no. 9, pp. 6222-6235, 2011.
7. J. Haupt, R. Baraniuk, R. Castro, and R. Nowak, "Compressive distilled sensing: Sparse recovery using adaptivity in compressive measurements," in Proc. Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Nov 2009, pp. 1551-1555.
8. P. Indyk, E. Price, and D.P Woodruff, "On the power of adaptivity in sparse recovery," in Proc. IEEE Foundations of Computer Science (FOCS), Oct. 2011, pp. 285-294.
9. E. Arias-Castro, E. J. Candes, and M. Davenport, "On the fundamental limits of adaptive sensing," Manuscript, Nov. 2011.
10. M. Malloy and R. Nowak, "Sequential analysis in high dimensional multiple testing and sparse recovery," in Proc. Asilomar Conf. on Signals, Systems, and Computers, 2011, pp. 2661-2665.
11. A. Soni and J. Haupt, "Efficient adaptive compressive sensing using sparse hierarchical learned dictionaries," in Proc. Asilomar Conf. on Signals, Systems, and Computers, 2011, pp. 1250-1254.
12. M. Iwen and A. Tewfik, "Adaptive group testing strategies for target detection and localization in noisy environments," IEEE Trans. Signal Proc, Vol. 60, no. 5, pp. 2344-2353, 2012.
13. S. Muthukrishnan, Data streams: Algorithms and applications, Now Publishers, 2005.
14. P. Indyk and A. Gilbert, "Sparse recovery using sparse matrices," Proc. IEEE, Vol. 98, no. 6, pp. 937-947, 2010.
15. J. Haupt and R. Baraniuk, "Robust support recovery using sparse compressive sensing matrices," in Proc. 45th Annual Conf. on Information Sciences and Systems, Baltimore, MD, March 2011, pp. 1-6.