Compressive Sensing
暂无分享,去创建一个
[1] Sören Bartels,et al. Numerical Methods for Nonlinear Partial Differential Equations , 2015 .
[2] Holger Rauhut,et al. A Mathematical Introduction to Compressive Sensing , 2013, Applied and Numerical Harmonic Analysis.
[3] S. Foucart. A note on guaranteed sparse recovery via ℓ1-minimization , 2010 .
[4] Jared Tanner,et al. Phase Transitions for Greedy Sparse Approximation Algorithms , 2010, ArXiv.
[5] Holger Rauhut,et al. The Gelfand widths of lp-balls for 0p<=1 , 2010, J. Complex..
[6] David P. Woodruff,et al. Lower bounds for sparse recovery , 2010, SODA '10.
[7] Massimo Fornasier,et al. The application of joint sparsity and total variation minimization algorithms to a real-life art restoration problem , 2009, Adv. Comput. Math..
[8] Carola-Bibiane Schönlieb,et al. A convergent overlapping domain decomposition method for total variation minimization , 2009, Numerische Mathematik.
[9] Thomas Strohmer,et al. Compressed Remote Sensing of Sparse Objects , 2009, SIAM J. Imaging Sci..
[10] Holger Rauhut,et al. Compressive estimation of doubly selective channels: exploiting channel sparsity to improve spectral efficiency in multicarrier transmissions , 2009, ArXiv.
[11] Holger Rauhut,et al. Circulant and Toeplitz matrices in compressed sensing , 2009, ArXiv.
[12] Justin K. Romberg,et al. Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.
[13] Yaakov Tsaig,et al. Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.
[14] M. Rudelson,et al. On sparse reconstruction from Fourier and Gaussian measurements , 2008 .
[15] Jan Vybíral,et al. Widths of embeddings in function spaces , 2008, J. Complex..
[16] R. DeVore,et al. Compressed sensing and best k-term approximation , 2008 .
[17] I. Daubechies,et al. Iteratively reweighted least squares minimization for sparse recovery , 2008, 0807.0575.
[18] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..
[19] Thomas Strohmer,et al. Compressed sensing radar , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[20] Mike E. Davies,et al. Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.
[21] Pierre Vandergheynst,et al. Dictionary Preconditioning for Greedy Algorithms , 2008, IEEE Transactions on Signal Processing.
[22] Piotr Indyk,et al. Combining geometry and combinatorics: A unified approach to sparse signal recovery , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.
[23] J. Romberg,et al. Imaging via Compressive Sampling , 2008, IEEE Signal Processing Magazine.
[24] E.J. Candes,et al. An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.
[25] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.
[26] Ting Sun,et al. Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..
[27] Richard Baraniuk. Compressive sensing , 2008, CISS.
[28] Jean-Luc Starck,et al. Compressed Sensing in Astronomy , 2008, IEEE Journal of Selected Topics in Signal Processing.
[29] R. DeVore,et al. A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .
[30] Massimo Fornasier,et al. Restoration of Color Images by Vector Valued BV Functions and Variational Calculus , 2007, SIAM J. Appl. Math..
[31] Holger Rauhut,et al. Sparsity in Time-Frequency Representations , 2007, ArXiv.
[32] Pablo A. Parrilo,et al. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..
[33] R. Vershynin,et al. One sketch for all: fast algorithms for compressed sensing , 2007, STOC '07.
[34] H. Rauhut. Stability Results for Random Sampling of Sparse Trigonometric Polynomials , 2006, IEEE Transactions on Information Theory.
[35] S. Mendelson,et al. Uniform Uncertainty Principle for Bernoulli and Subgaussian Ensembles , 2006, math/0608665.
[36] D. Donoho,et al. Counting faces of randomly-projected polytopes when the projection radically lowers dimension , 2006, math/0607364.
[37] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[38] Graham Cormode,et al. Combinatorial Algorithms for Compressed Sensing , 2006, 2006 40th Annual Conference on Information Sciences and Systems.
[39] Joel A. Tropp,et al. Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.
[40] H. Rauhut. Random Sampling of Sparse Trigonometric Polynomials , 2005, math/0512642.
[41] Stephen P. Boyd,et al. Convex Optimization , 2010, IEEE Transactions on Automatic Control.
[42] E. Candès,et al. Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.
[43] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[44] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[45] E. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[46] Jean-Jacques Fuchs,et al. On sparse representations in arbitrary redundant bases , 2004, IEEE Transactions on Information Theory.
[47] H. Bungartz,et al. Sparse grids , 2004, Acta Numerica.
[48] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[49] Rémi Gribonval,et al. Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.
[50] 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.
[51] Thomas Strohmer,et al. GRASSMANNIAN FRAMES WITH APPLICATIONS TO CODING AND COMMUNICATION , 2003, math/0301135.
[52] S. Muthukrishnan,et al. Approximation of functions over redundant dictionaries using coherence , 2003, SODA '03.
[53] O. Christensen. An introduction to frames and Riesz bases , 2002 .
[54] Michael Elad,et al. A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.
[55] Sudipto Guha,et al. Near-optimal sparse fourier representations via sampling , 2002, STOC '02.
[56] Avi Wigderson,et al. Randomness conductors and constant-degree lossless expanders , 2002, STOC '02.
[57] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[58] Dimitris Achlioptas,et al. Database-friendly random projections , 2001, PODS.
[59] M. R. Osborne,et al. A new approach to variable selection in least squares problems , 2000 .
[60] M. R. Osborne,et al. On the LASSO and its Dual , 2000 .
[61] M. Unser. Sampling-50 years after Shannon , 2000, Proceedings of the IEEE.
[62] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[63] M. Talagrand. Selecting a proportion of characters , 1998 .
[64] Bhaskar D. Rao,et al. Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..
[65] Balas K. Natarajan,et al. Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..
[66] Erich Novak,et al. Optimal Recovery and n-Widths for Convex Classes of Functions , 1995 .
[67] P. Schmieder,et al. Application of nonlinear sampling schemes to COSY-type spectra , 1993, Journal of biomolecular NMR.
[68] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[69] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[70] B. Logan,et al. Signal recovery and the large sieve , 1992 .
[71] Rolf Schneider,et al. Random projections of regular simplices , 1992, Discret. Comput. Geom..
[72] Henryk Wozniakowski,et al. Information-based complexity , 1987, Nature.
[73] W. M. Carey,et al. Digital spectral analysis: with applications , 1986 .
[74] Charles R. Johnson,et al. Matrix analysis , 1985 .
[75] E. Gluskin. NORMS OF RANDOM MATRICES AND WIDTHS OF FINITE-DIMENSIONAL SETS , 1984 .
[76] B. S. Kašin,et al. DIAMETERS OF SOME FINITE-DIMENSIONAL SETS AND CLASSES OF SMOOTH FUNCTIONS , 1977 .
[77] A. Sterrett. On the Detection of Defective Members of Large Populations , 1957 .
[78] Holger Rauhut,et al. The Gelfand widths of ℓp-balls for 0 , 2010, ArXiv.
[79] Massimo Fornasier,et al. Numerical Methods for Sparse Recovery , 2010 .
[80] H. Rauhut. Compressive Sensing and Structured Random Matrices , 2009 .
[81] David L. Donoho,et al. High-Dimensional Centrally Symmetric Polytopes with Neighborliness Proportional to Dimension , 2006, Discret. Comput. Geom..
[82] E.J. Candes. Compressive Sampling , 2022 .
[83] D. Donoho,et al. Neighborliness of randomly projected simplices in high dimensions. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[84] G. Lorentz,et al. Constructive approximation : advanced problems , 1996 .
[85] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[86] Yurii Nesterov,et al. Interior-point polynomial algorithms in convex programming , 1994, Siam studies in applied mathematics.
[87] J. Kuelbs. Probability on Banach spaces , 1978 .
[88] A. K. Cline. Rate of Convergence of Lawson's Algorithm , 1972 .
[89] Holger Rauhut,et al. Edinburgh Research Explorer Identification of Matrices Having a Sparse Representation , 2022 .