Correlation-aware sparse support recovery: Gaussian sources

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 5880-5884

  Pal, Piya ^a   Vaidyanathan, P P ^a  

^a California Institute of Technology   (United States)

Author keywords

Block Sparsity; Correlation; LASSO; Multiple Measurement Vector (MMV); Support Recovery

Indexed keywords

BLOCK SPARSITIES; GAUSSIAN SOURCES; IDEAL CORRELATION; LASSO; MEASUREMENT MATRIX; MULTIPLE MEASUREMENT VECTOR (MMV); PROBABILITY OF SUCCESS; SUPPORT RECOVERIES;

CORRELATION METHODS; COVARIANCE MATRIX; PROBABILITY DISTRIBUTIONS; SIGNAL PROCESSING; VECTORS;

RECOVERY;

EID: 84890456724     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6638792     Document Type: Conference Paper

References (12)

1. Zhilin Zhang, and B.D. Rao, "Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning", IEEE Journal of Selected Topics in Signal Processing, vol.5, no.5, pp. 912-926, Sep. 2011.
2. S. F. Cotter, B. D. Rao, Engan Kjersti, K. Kreutz-Delgado, "Sparse solutions to linear inverse problems with multiple measurement vectors", IEEE Transactions on Signal Processing, vol. 53, no. 7, pp. 2477-2488, July 2005.
3. Kiryung Lee, Y. Bresler, and M. Junge, "Subspace Meth-ods for Joint Sparse Recovery", IEEE Transactions on Information Theory, vol.58, no.6, pp. 3613-3641, June 2012.
4. M. Stojnic, F. Parvaresh, B. Hassibi, "On the Reconstruc-tion of Block-Sparse Signals With an Optimal Number of Measurements", IEEE Trans. SP, vol. 57, no. 8, pp. 3075-3085, Aug. 2009.
5. Jie Chen, Xiaoming Huo, "Theoretical Results on Sparse Representations of Multiple-Measurement Vec-tors," IEEE Transactions on Signal Processing, vol. 54, no. 12, pp. 4634-4643, Dec. 2006.
6. J. Haupt,W.U. Bajwa, G. Raz, R. Nowak, "Toeplitz Com-pressed Sensing Matrices With Applications to Sparse Channel Estimation", IEEE Transactions on Information Theory, vol. 56, no. 11, pp.5862-5875, Nov. 2010.
7. J. J. Fuchs, "Recovery of exact sparse representations in the presence of bounded noise," Information Theory, IEEE Transactions on, vol. 51, no. 10, pp. 3601-3608, Oct. 2005.
8. M. J. Wainwright, "Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using l1-Constrained Quadratic Programming (Lasso)," IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 2183-2202, May 2009.
9. D. Malioutov, M. Cetin, and A. S. Willsky, "A sparse signal reconstruction perspective for source localization with sensor arrays",IEEE Transactions on Signal Pro-cessing, vol. 53, no. 8, pp. 3010-3022, Aug. 2005.
10. Piya Pal and P. P. Vaidyanathan, "Correlation-Aware Techniques for Sparse Support Recovery", IEEE Statis-tical Signal Processing Workshop, 2012, Ann Arbor, MI, Aug. 5-8, 2012.
11. Piya Pal and P. P. Vaidyanathan, "On Application of LASSO for Sparse Support Recovery With Imperfect Correlation Awareness", Proc. of 46th Asilomar Confer-ence on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 4-7, 2012.
12. Piya Pal and P. P. Vaidyanathan, "Improving the lim-its of sparse support recovery in Multiple Measurement Vector (MMV) models using correlation-awareness", in preparation.