Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse
暂无分享,去创建一个
[1] N. Sloane,et al. Hadamard transform optics , 1979 .
[2] William L. Briggs,et al. The DFT : An Owner's Manual for the Discrete Fourier Transform , 1987 .
[3] B. Logan,et al. Signal recovery and the large sieve , 1992 .
[4] P. Schmieder,et al. Application of nonlinear sampling schemes to COSY-type spectra , 1993, Journal of biomolecular NMR.
[5] P. Schmieder,et al. Improved resolution in triple-resonance spectra by nonlinear sampling in the constant-time domain , 1994, Journal of biomolecular NMR.
[6] David L. Donoho,et al. WaveLab and Reproducible Research , 1995 .
[7] Gene H. Golub,et al. Matrix computations (3rd ed.) , 1996 .
[8] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[9] Alan S. Stern,et al. NMR Data Processing , 1996 .
[10] S. Mallat,et al. Adaptive greedy approximations , 1997 .
[11] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[12] M. R. Osborne,et al. On the LASSO and its Dual , 2000 .
[13] M. R. Osborne,et al. A new approach to variable selection in least squares problems , 2000 .
[14] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[15] Michael Elad,et al. A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.
[16] Sudipto Guha,et al. Near-optimal sparse fourier representations via sampling , 2002, STOC '02.
[17] S. Vasanawala,et al. 3 D Fluid-Suppressed T 2-Prep Flow-Independent Angiography using Balanced SSFP , 2002 .
[18] Peter Hawkes,et al. Advances in Imaging and Electron Physics , 2002 .
[19] 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.
[20] I. Daubechies,et al. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.
[21] Thomas Strohmer,et al. GRASSMANNIAN FRAMES WITH APPLICATIONS TO CODING AND COMMUNICATION , 2003, math/0301135.
[22] Jean-Luc Starck,et al. Image decomposition: separation of texture from piecewise smooth content , 2003, SPIE Optics + Photonics.
[23] Rémi Gribonval,et al. Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.
[24] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[25] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[26] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[27] Jean-Jacques Fuchs,et al. On sparse representations in arbitrary redundant bases , 2004, IEEE Transactions on Information Theory.
[28] D. Donoho,et al. Redundant Multiscale Transforms and Their Application for Morphological Component Separation , 2004 .
[29] J. Tropp. JUST RELAX: CONVEX PROGRAMMING METHODS FOR SUBSET SELECTION AND SPARSE APPROXIMATION , 2004 .
[30] Mark D. Plumbley. Geometry and homotopy for l 1 sparse representations , 2005 .
[31] David L. Donoho,et al. Neighborly Polytopes And Sparse Solution Of Underdetermined Linear Equations , 2005 .
[32] E. Candès,et al. Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.
[33] Michael Elad,et al. Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .
[34] J. Tropp,et al. SIGNAL RECOVERY FROM PARTIAL INFORMATION VIA ORTHOGONAL MATCHING PURSUIT , 2005 .
[35] Emmanuel J. Candès,et al. Signal recovery from random projections , 2005, IS&T/SPIE Electronic Imaging.
[36] Justin Romberg,et al. Practical Signal Recovery from Random Projections , 2005 .
[37] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[38] Dmitry M. Malioutov,et al. Homotopy continuation for sparse signal representation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[39] M. Rudelson,et al. Geometric approach to error-correcting codes and reconstruction of signals , 2005, math/0502299.
[40] D. Donoho,et al. Counting faces of randomly-projected polytopes when the projection radically lowers dimension , 2006, math/0607364.
[41] Yaakov Tsaig,et al. Extensions of compressed sensing , 2006, Signal Process..
[42] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[43] D. Donoho. For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .
[44] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[45] Michael Elad,et al. Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.
[46] A. Banerjee. Convex Analysis and Optimization , 2006 .
[47] Joel A. Tropp,et al. Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.
[48] David L. Donoho,et al. Sparse Solution Of Underdetermined Linear Equations By Stagewise Orthogonal Matching Pursuit , 2006 .
[49] David L. Donoho,et al. High-Dimensional Centrally Symmetric Polytopes with Neighborliness Proportional to Dimension , 2006, Discret. Comput. Geom..
[50] Yaakov Tsaig,et al. Breakdown of equivalence between the minimal l1-norm solution and the sparsest solution , 2006, Signal Process..
[51] Mark D. Plumbley. Recovery of Sparse Representations by Polytope Faces Pursuit , 2006, ICA.
[52] David L. Donoho,et al. Solution of l1Minimization Problems by LARS/Homotopy Methods , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[53] R. Gribonval,et al. Highly sparse representations from dictionaries are unique and independent of the sparseness measure , 2007 .
[54] Michael Elad,et al. Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization , 2007 .
[55] Joel A. Tropp,et al. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.