CG-M-FOCUSS and Its Application to Distributed Compressed Sensing
暂无分享,去创建一个
[1] Bhaskar D. Rao,et al. An affine scaling methodology for best basis selection , 1999, IEEE Trans. Signal Process..
[2] Michael Zibulevsky,et al. Underdetermined blind source separation using sparse representations , 2001, Signal Process..
[3] Bhaskar D. Rao,et al. Signal processing with the sparseness constraint , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[4] R.G. Baraniuk,et al. Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.
[5] Ting Sun,et al. Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..
[6] I F Gorodnitsky,et al. Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. , 1995, Electroencephalography and clinical neurophysiology.
[7] M. Lustig,et al. Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.
[8] Stephen P. Boyd,et al. An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.
[9] Joseph F. Murray,et al. Dictionary Learning Algorithms for Sparse Representation , 2003, Neural Computation.
[10] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[11] Stephen J. Wright,et al. Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .
[12] R.G. Baraniuk,et al. Distributed Compressed Sensing of Jointly Sparse Signals , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..
[13] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[14] Mineichi Kudo,et al. Performance analysis of minimum /spl lscr//sub 1/-norm solutions for underdetermined source separation , 2004, IEEE Transactions on Signal Processing.
[15] K. Kreutz-Delgado,et al. Deriving algorithms for computing sparse solutions to linear inverse problems , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).
[16] Andrzej Cichocki,et al. Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .
[17] Yuanqing Li,et al. Analysis of Sparse Representation and Blind Source Separation , 2004, Neural Computation.
[18] Bhaskar D. Rao,et al. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..
[19] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[20] D K Smith,et al. Numerical Optimization , 2001, J. Oper. Res. Soc..
[21] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[22] Bhaskar D. Rao,et al. Sparse solutions to linear inverse problems with multiple measurement vectors , 2005, IEEE Transactions on Signal Processing.
[23] D. Donoho,et al. Maximal Sparsity Representation via l 1 Minimization , 2002 .
[24] Richard G. Baraniuk,et al. Compressive Sensing , 2008, Computer Vision, A Reference Guide.
[25] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[26] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[27] Daniel W. C. Ho,et al. Underdetermined blind source separation based on sparse representation , 2006, IEEE Transactions on Signal Processing.
[28] M. Hestenes,et al. Methods of conjugate gradients for solving linear systems , 1952 .