Recovery guarantees for mixed norm ℓp1, p2 block sparse representations
暂无分享,去创建一个
[1] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[2] Yonina C. Eldar,et al. Block-Sparse Signals: Uncertainty Relations and Efficient Recovery , 2009, IEEE Transactions on Signal Processing.
[3] Rémi Gribonval,et al. Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.
[4] Babak Hassibi,et al. Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays , 2008, IEEE Journal of Selected Topics in Signal Processing.
[5] 24th European Signal Processing Conference, EUSIPCO 2016, Budapest, Hungary, August 29 - September 2, 2016 , 2016, European Signal Processing Conference.
[6] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[7] Michael Elad,et al. A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.
[8] D. Donoho,et al. Uncertainty principles and signal recovery , 1989 .
[9] Jon Feldman,et al. Decoding error-correcting codes via linear programming , 2003 .
[10] S. Geer,et al. High-dimensional data: p >> n in mathematical statistics and bio-medical applications , 2004 .
[11] Yonina C. Eldar,et al. Robust Recovery of Signals From a Structured Union of Subspaces , 2008, IEEE Transactions on Information Theory.
[12] M. Lustig,et al. Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.
[13] Yonina C. Eldar,et al. From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals , 2009, IEEE Journal of Selected Topics in Signal Processing.
[14] 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.
[15] Rick Chartrand,et al. Exact Reconstruction of Sparse Signals via Nonconvex Minimization , 2007, IEEE Signal Processing Letters.
[16] Rémi Gribonval,et al. Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$ , 2009, IEEE Transactions on Information Theory.
[17] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[18] Davies Rémi Gribonval. Restricted Isometry Constants Where Lp Sparse Recovery Can Fail for 0 , 2008 .
[19] M. Davies. The Restricted Isometry Property and ` p sparse recovery failure , 2009 .
[20] D. Donoho. Superresolution via sparsity constraints , 1992 .
[21] H Ans C. Va. High-dimensional data: p n in mathematical statistics and bio-medical applications , 2004 .
[22] Robert D. Nowak,et al. Signal Reconstruction From Noisy Random Projections , 2006, IEEE Transactions on Information Theory.
[23] S. Frick,et al. Compressed Sensing , 2014, Computer Vision, A Reference Guide.
[24] S. Foucart,et al. Sparsest solutions of underdetermined linear systems via ℓq-minimization for 0 , 2009 .
[25] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.