Expectation-maximization Gaussian-mixture approximate message passing

2012 46th Annual Conference on Information Sciences and Systems, CISS 2012

Volumn , Issue , 2012, Pages

  Vila, Jeremy ^a   Schniter, Philip ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APRIORI; EXPECTATION MAXIMIZATION; GAUSSIAN MIXTURES; LINEAR MEASUREMENTS; MEAN SQUARED ERROR; MINIMAX; NON-ZERO COEFFICIENTS; NUMERICAL EXPERIMENTS; ROBUST ALGORITHM; SIGNAL CLASS; SIGNAL DISTRIBUTION; SPARSE SIGNALS; STATE-OF-THE-ART PERFORMANCE;

INFORMATION SCIENCE; MAXIMUM PRINCIPLE; SIGNAL PROCESSING;

RECOVERY;

EID: 84868564170     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISS.2012.6310932     Document Type: Conference Paper

References (18)

1. Y. C. Eldar and G. Kutyniok, Compressed Sensing: TheOlY and Applications. New York: Cambridge Univ. Press, 2012.
2. R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Roy. Statist. Soc. B, vol. 58, no. I, pp. 267-288, 1996.
3. D. L. Donoho, A. Maleki, and A. Montanari, "Message passing algorithms for compressed sensing," Proc. Nat. Acad. SCi., vol. 106, pp. 18914-18919, Nov. 2009.
4. D. L. Donoho, A. Maleki, and A. Montanari, "The noise-sensitivity phase transition in compressed sensing," arXiv:l004.1218, Apr. 2010.
5. Y. Wu and S. Verdu, "Optimal phase transitions in compressed sensing," arXiv:1JJ1.6822, Nov. 2011.
6. D. L. Donoho, A. Maleki, and A. Montanari, "Message passing algorithms for compressed sensing: I. Motivation and construction," in Proc. Inform. Theory Workshop, (Cairo, Egypt), Jan. 2010.
7. S. Rangan, "Generalized approximate message passing for estimation with random linear mixing," arXiv:IOIO.5141, Oct. 2010.
8. A. Dempster, N. M. Laird, and D. B. Rubin, "Maximum-likelihood from incomplete data via the EM algorithm," J. Roy. Statist. Soc., vol. 39, pp. 1-17, 1977.
9. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. I, pp. 211-244, 2001.
10. D. P. Wipf and B. D. Rao, "Sparse Bayesian learning for basis selection," IEEE Trans. Signal Process., vol. 52, pp. 2153-2164, Aug. 2004.
11. S. Ji, Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. Signal Process., vol. 56, pp. 2346-2356, June 2008.
12. J. P. Vila and P. Schniter, "Expectation-maximization Bernoulli-Gaussian approximate message passing," in Proc. Asilomar Collf Signals Syst. Comput., (Pacific Grove, CAl, Nov. 2011.
13. R. Neal and G. Hinton, "A view of the EM algorithm that justifies incremental, sparse, and other variants," in Learning in Graphical Models (M. I. Jordan, ed.), pp. 355-368, MIT Press, 1999.
14. N. Ueda, R. Nakano, Z. Ghahramani, and G. E. Hinton, "SMEM algorithm for mixture models," Neural Comput., vol. 12, pp. 2109-2128, Sept. 2000.
15. Z. Zhang and B. D. Rao, "Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning," JEEE J. Sel. Topics Signal Process., vol. 5, pp. 912-926, Sept. 20 II.
16. E. van den Berg and M. P. Friedlander, "Probing the Pareto frontier for basis pursuit solutions," SIAM J. Scientific Comput., vol. 31, no. 2, pp. 890-912, 2008.
17. I-I. Mohimani, M. Babaie-Zadeh, and C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed norm," IEEE Trans. Signal Process., vol. 57, pp. 289-301, Jan. 2009.
18. V. Cevher, "Learning with compressible priors," in Proc. Neural flr/orm. Process. Syst. Conf, (Vancouver, B.C.), Dec. 2009.