An empirical-bayes approach to recovering linearly constrained non-negative sparse signals

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Volumn , Issue , 2013, Pages 5-8

  Vila, Jeremy ^a   Schniter, Philip ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84894154978     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CAMSAP.2013.6713993     Document Type: Conference Paper

References (16)

1. J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches", IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 2, pp. 354-379, 2012.
2. H. Markowitz, Portfolio Selection: Efficient Diversification of Investments. New York: Wiley, 1991.
3. A. Kyrillidis, S. Becker, V. Cevher, and C. Koch, "Sparse projections onto the simplex", arXiv:1206.1529, Apr. 2013.
4. 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.
5. S. Rangan, "Generalized approximate message passing for estimation with random linear mixing", in Proc. IEEE Int. Symp. Inform. Thy., (Saint Petersburg, Russia), pp. 2168-2172, Aug. 2011. (Full version at arXiv:1010.5141).
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), pp. 1-5, Jan. 2010.
7. A. Javanmard and A. Montanari, "State evolution for general approximate message passing algorithms, with applications to spatial coupling", arXiv:1211.5164, Nov. 2012.
8. H. Monajemi, S. Jafarpour, M. Gavish, Stat 330/CME 362 Collaboration, and D. L. Donoho, "Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices", Proc. Nat. Acad. Sci., vol. 110, pp. 1181-1186, Jan. 2013.
9. J. P. Vila and P. Schniter, "Expectation-maximization Gaussian-mixture approximate message passing", IEEE Trans. Signal Process., vol. 61, pp. 4658-4672, Oct. 2013.
10. B. Efron, Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. New York: Cambridge University Press, 2010.
11. J. P. Vila and P. Schniter, "An empirical-Bayes approach to recovering linearly constrained non-negative sparse signals", arXiv:1310.2806, 2013.
12. 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.
13. D. L. Donoho and J. Tanner, "Sparse nonnegative solution of underdetermined linear equations by linear programming", Proc. Nat. Acad. Sci, vol. 102, no. 27, pp. 9446-9451, 2005.
14. C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2007.
15. 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.
16. S. Becker, E. Candes, and M. M. Grant, "Templates for convex cone problems with applications to sparse signal recovery", Math. Program. Comput, vol. 3, no. 3, pp. 165-218, 2011.