Donoho D. Compressed sensing[J]. IEEE Transactions Information Theory, 2006,52 ( 4 ) : 1289-1306.
[2]
Candes E J, Romberg J, Tao T. Signal recovery from incomplete and inaccurate measurements [J]. Communications on Pure and Applied Mathematics, 2005,59 ( 8 ) : 1207-1223.
[3]
Donoho D, Huo X. Uncertainty principles and ideal atomic decompositions[J]. IEEE Transactions on Information Theory, 2001,47 ( 7 ) :2845-2862.
[4]
Donoho D, Elad M. Maximal sparsity representation via 11 minimization [J]. Proceedings of the National Academy of Science, 2003,100(5 ) :2197-2202.
[5]
Candes E J, Tan T. Decoding by linear programming [J]. IEEE Transactions on Information Theory, 2005, 51(12) :4203-4215.
[6]
Tropp J, Gilbert A. Signal recovery from partial information via orthogonal matching pursuit [J]. IEEE Transactions on Information Theory, 2007, 53( 12): 4566-4666.
[7]
Tipping M. The relevance vector machine [ A]. In: Solla S A, Leen T K, Muller K-R. Advances in Neural Information Processing Systems 12[C], Cambridge, MA,USA:MIT Press, 2000: 652-658.
[8]
Figueiredo M. Adaptive sparseness for supervised learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9) : 1150-1159.
[9]
Vapnik V N. The Nature of Statistical Learning Theory[M]. New York: Springer2Verlag, 1995.
[10]
Bernardo J M, Smith A F M. Bayesian Theory[M]. Wiley,1994.
[11]
Faul A, Tipping M. Analysis of sparse bayesian learning [ A ]. In: Dietterich T G, Becker S, Ghahramani Z. Advances in Neural Information Processing Systems 14 [C] , Cambridge, MA, USA: MIT Press ,2002:383-389.
[12]
Tsaig Y, Donoho D. Extensions of compressed sensing [J]. Signal Processing, 2006,86(3) : 549-571.
