RIP-Based Near-Oracle Performance Guarantees for SP, CoSaMP, and IHT
暂无分享,去创建一个
[1] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.
[2] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[3] Michael Elad,et al. RIP-Based Near-Oracle Performance Guarantees for Subspace-Pursuit, CoSaMP, and Iterative Hard-Thresholding , 2010, 1005.4539.
[4] S. Foucart. A note on guaranteed sparse recovery via ℓ1-minimization , 2010 .
[5] Emmanuel J. Cand. Modern statistical estimation via oracle inequalities , 2006 .
[6] H. Rauhut. On the Impossibility of Uniform Sparse Reconstruction using Greedy Methods , 2007 .
[7] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[8] Olgica Milenkovic,et al. Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.
[9] Michael Elad,et al. From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..
[10] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[11] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[12] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[13] Deanna Needell,et al. Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit , 2007, IEEE Journal of Selected Topics in Signal Processing.
[14] Lie Wang,et al. Stable Recovery of Sparse Signals and an Oracle Inequality , 2010, IEEE Transactions on Information Theory.
[15] Mike E. Davies,et al. Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.
[16] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[17] Yonina C. Eldar,et al. Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise , 2009, IEEE Transactions on Signal Processing.