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[1] Emil Y. Sidky,et al. Practical iterative image reconstruction in digital breast tomosynthesis by non-convex TpV optimization , 2008, SPIE Medical Imaging.
[2] Joel A. Tropp,et al. Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.
[3] Donald Geman,et al. Nonlinear image recovery with half-quadratic regularization , 1995, IEEE Trans. Image Process..
[4] Xiaotong Shen,et al. Journal of the American Statistical Association Likelihood-based Selection and Sharp Parameter Estimation Likelihood-based Selection and Sharp Parameter Estimation , 2022 .
[5] Cun-Hui Zhang,et al. Rate Minimaxity of the Lasso and Dantzig Selector for the lq Loss in lr Balls , 2010, J. Mach. Learn. Res..
[6] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[7] Naoki Saito. Superresolution of noisy band-limited data by data adaptive regularization and its application to seismic trace inversion , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[8] Armando Manduca,et al. Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic $\ell_{0}$ -Minimization , 2009, IEEE Transactions on Medical Imaging.
[9] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[10] Jianqing Fan,et al. Nonconcave penalized likelihood with a diverging number of parameters , 2004, math/0406466.
[11] Martin J. Wainwright,et al. Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso) , 2009, IEEE Transactions on Information Theory.
[12] Tong Zhang,et al. Analysis of Multi-stage Convex Relaxation for Sparse Regularization , 2010, J. Mach. Learn. Res..
[13] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[14] Rick Chartrand,et al. Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[15] Tong Zhang,et al. A General Theory of Concave Regularization for High-Dimensional Sparse Estimation Problems , 2011, 1108.4988.
[16] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[17] Wei Pan,et al. On constrained and regularized high-dimensional regression , 2013, Annals of the Institute of Statistical Mathematics.
[18] Emmanuel J. Candès,et al. A Probabilistic and RIPless Theory of Compressed Sensing , 2010, IEEE Transactions on Information Theory.
[19] T. Hastie,et al. SparseNet: Coordinate Descent With Nonconvex Penalties , 2011, Journal of the American Statistical Association.
[20] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[21] 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.
[22] Stephen P. Boyd,et al. Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.
[23] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[24] Tong Zhang,et al. Sparse Recovery With Orthogonal Matching Pursuit Under RIP , 2010, IEEE Transactions on Information Theory.
[25] Changshui Zhang,et al. High-dimensional Inference via Lipschitz Sparsity-Yielding Regularizers , 2013, AISTATS.
[26] Tong Zhang. Multi-stage Convex Relaxation for Feature Selection , 2011, 1106.0565.
[27] Ting Sun,et al. Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..
[28] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[29] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[30] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[31] Armando Manduca,et al. Sparse MRI Reconstruction via Multiscale L0-Continuation , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.
[32] Cun-Hui Zhang,et al. Adaptive Lasso for sparse high-dimensional regression models , 2008 .
[33] Rahul Garg,et al. Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property , 2009, ICML '09.
[34] Xiaojun Chen,et al. Complexity of unconstrained $$L_2-L_p$$ minimization , 2011, Math. Program..
[35] Yoram Bresler,et al. Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography , 1998, IEEE Trans. Image Process..
[36] R. DeVore,et al. A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .
[37] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[38] Martin J. Wainwright,et al. Restricted Eigenvalue Properties for Correlated Gaussian Designs , 2010, J. Mach. Learn. Res..
[39] Michael Elad,et al. Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.
[40] J Trzasko,et al. Nonconvex prior image constrained compressed sensing (NCPICCS): theory and simulations on perfusion CT. , 2011, Medical physics.
[41] Armando Manduca,et al. Relaxed Conditions for Sparse Signal Recovery With General Concave Priors , 2009, IEEE Transactions on Signal Processing.
[42] Xiaochuan Pan,et al. Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. , 2009, Medical physics.
[43] Tong Zhang,et al. A General Framework of Dual Certificate Analysis for Structured Sparse Recovery Problems , 2012, 1201.3302.
[44] V. Koltchinskii. The Dantzig selector and sparsity oracle inequalities , 2009, 0909.0861.
[45] D. Hunter,et al. Variable Selection using MM Algorithms. , 2005, Annals of statistics.
[46] Xiaochuan Pan,et al. Image reconstruction from few views by non-convex optimization , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.
[47] S. Geer,et al. On the conditions used to prove oracle results for the Lasso , 2009, 0910.0722.
[48] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[49] K. Lange,et al. Coordinate descent algorithms for lasso penalized regression , 2008, 0803.3876.
[50] Joel A. Tropp,et al. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.
[51] S. Foucart,et al. Sparsest solutions of underdetermined linear systems via ℓq-minimization for 0 , 2009 .
[52] Zhihua Zhang,et al. A non-convex relaxation approach to sparse dictionary learning , 2011, CVPR 2011.
[53] Francis R. Bach,et al. Structured sparsity-inducing norms through submodular functions , 2010, NIPS.
[54] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..