Expectation-maximization Bernoulli-Gaussian approximate message passing

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn , Issue , 2011, Pages 799-803

  Vila, Jeremy ^a   Schniter, Philip ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATTRACTIVE SOLUTIONS; COMPRESSIVE SENSING; EXCELLENT PERFORMANCE; EXPECTATION MAXIMIZATION; GAUSSIANS; LEAST SQUARE; MARGINAL DISTRIBUTION; NUMERICAL EXPERIMENTS; SIGNAL AND NOISE PARAMETERS; SIGNAL DISTRIBUTION; SPACE BETWEEN;

GAUSSIAN DISTRIBUTION; MAXIMUM PRINCIPLE; SIGNAL PROCESSING;

ALGORITHMS;

EID: 84861322335     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.2011.6190117     Document Type: Conference Paper

References (14)

1. R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Roy. Statist. Soc. B, vol. 58, no. 1, pp. 267-288, 1996.
2. D. L. Donoho, A. Maleki, and A. Montanari, "Message passing algorithms for compressed sensing," Proc. National Academy of Sciences, vol. 106, pp. 18914-18919, Nov. 2009.
3. D. L. Donoho, A. Maleki, and A. Montanari, "The noise-sensitivity phase transition in compressed sensing," arXiv:1004.1218, Apr. 2010.
4. 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.
5. M. Bayati and A. Montanari, "The dynamics of message passing on dense graphs, with applications to compressed sensing," IEEE Trans. Inform. Theory, vol. 57, pp. 764-785, Feb. 2011.
6. S. Rangan, "Generalized approximate message passing for estimation with random linear mixing," arXiv:1010.5141, Oct. 2010.
7. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Machine Learning Research, vol. 1, pp. 211-244, 2001.
8. D. Wipf and B. Rao, "Sparse Bayesian learning for basis selection,"IEEE Trans. Signal Process., vol. 52, pp. 2153-2164, Aug. 2004.
9. S. Ji, Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. Signal Process., vol. 56, pp. 2346-2356, June 2008.
10. 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.
11. 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.
12. 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.
13. H. 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.
14. V. Cevher, "Learning with compressible priors," in Proc. Neural Inform. Process. Syst. Conf., (Vancouver, B.C.), Dec. 2009.