Estimating structured signals in sparse noise: A precise noise sensitivity analysis
暂无分享,去创建一个
[1] Andrea Montanari,et al. The LASSO Risk for Gaussian Matrices , 2010, IEEE Transactions on Information Theory.
[2] 丸山 徹. Convex Analysisの二,三の進展について , 1977 .
[3] Christos Thrampoulidis,et al. A Tight Version of the Gaussian min-max theorem in the Presence of Convexity , 2014, ArXiv.
[4] Mihailo Stojnic,et al. A rigorous geometry-probability equivalence in characterization of ℓ1-optimization , 2013, ArXiv.
[5] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[6] Babak Hassibi,et al. Asymptotically Exact Denoising in Relation to Compressed Sensing , 2013, ArXiv.
[7] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[8] John Wright,et al. Dense Error Correction Via $\ell^1$-Minimization , 2010, IEEE Transactions on Information Theory.
[9] Mihailo Stojnic,et al. Various thresholds for ℓ1-optimization in compressed sensing , 2009, ArXiv.
[10] Lie Wang. The L1L1 penalized LAD estimator for high dimensional linear regression , 2013, J. Multivar. Anal..
[11] Andrea Montanari,et al. The Noise-Sensitivity Phase Transition in Compressed Sensing , 2010, IEEE Transactions on Information Theory.
[12] Trac D. Tran,et al. Exact Recoverability From Dense Corrupted Observations via $\ell _{1}$-Minimization , 2011, IEEE Transactions on Information Theory.
[13] MaYi,et al. Dense error correction via l1-minimization , 2010 .
[14] D. Sengupta. Linear models , 2003 .
[15] Christos Thrampoulidis,et al. The squared-error of generalized LASSO: A precise analysis , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[16] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[17] J. Powell,et al. Least absolute deviations estimation for the censored regression model , 1984 .
[18] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[19] Rina Foygel,et al. Corrupted Sensing: Novel Guarantees for Separating Structured Signals , 2013, IEEE Transactions on Information Theory.
[20] Christos Thrampoulidis,et al. Simple Bounds for Noisy Linear Inverse Problems with Exact Side Information , 2013, ArXiv.
[21] Y. Gordon. On Milman's inequality and random subspaces which escape through a mesh in ℝ n , 1988 .
[22] Joel A. Tropp,et al. The Bowling Scheme , 2014 .
[23] Joel A. Tropp,et al. Living on the edge: A geometric theory of phase transitions in convex optimization , 2013, ArXiv.
[24] M. Talagrand,et al. Probability in Banach Spaces: Isoperimetry and Processes , 1991 .
[25] S. Stigler. The Asymptotic Distribution of the Trimmed Mean , 1973 .
[26] S. R. Searle. Linear Models , 1971 .
[27] R. Vershynin. Estimation in High Dimensions: A Geometric Perspective , 2014, 1405.5103.
[28] Christos Thrampoulidis,et al. Simple error bounds for regularized noisy linear inverse problems , 2014, 2014 IEEE International Symposium on Information Theory.
[29] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[30] Yihong Wu,et al. Shannon Theory for Compressed Sensing , 2011 .
[31] Pablo A. Parrilo,et al. The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.
[32] Mihailo Stojnic,et al. A framework to characterize performance of LASSO algorithms , 2013, ArXiv.
[33] D. Pollard. Asymptotics for Least Absolute Deviation Regression Estimators , 1991, Econometric Theory.