Universality in Learning from Linear Measurements
暂无分享,去创建一个
[1] Babak Hassibi,et al. A Universal Analysis of Large-Scale Regularized Least Squares Solutions , 2017, NIPS.
[2] M. Stojnic. Upper-bounding $\ell_1$-optimization weak thresholds , 2013 .
[3] Joel A. Tropp,et al. Universality laws for randomized dimension reduction, with applications , 2015, ArXiv.
[4] Andrea J. Goldsmith,et al. Estimation of simultaneously structured covariance matrices from quadratic measurements , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[5] Geert Leus,et al. Compressive Wideband Power Spectrum Estimation , 2012, IEEE Transactions on Signal Processing.
[6] Andrea J. Goldsmith,et al. Exact and Stable Covariance Estimation From Quadratic Sampling via Convex Programming , 2013, IEEE Transactions on Information Theory.
[7] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[8] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[9] Pablo A. Parrilo,et al. The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.
[10] Andrea Montanari,et al. Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.
[11] Joel A. Tropp,et al. Living on the edge: phase transitions in convex programs with random data , 2013, 1303.6672.
[12] Yonina C. Eldar,et al. Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices , 2012, IEEE Transactions on Information Theory.
[13] Joel A. Tropp,et al. An Introduction to Matrix Concentration Inequalities , 2015, Found. Trends Mach. Learn..
[14] Mihailo Stojnic,et al. Strong thresholds for ℓ2/ℓ1-optimization in block-sparse compressed sensing , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[15] Sujay Sanghavi,et al. The Local Convexity of Solving Systems of Quadratic Equations , 2015, 1506.07868.
[16] Christos Thrampoulidis,et al. Precise Error Analysis of Regularized $M$ -Estimators in High Dimensions , 2016, IEEE Transactions on Information Theory.
[17] Xiaodong Li,et al. Sparse Signal Recovery from Quadratic Measurements via Convex Programming , 2012, SIAM J. Math. Anal..
[18] Noureddine El Karoui,et al. Operator norm consistent estimation of large-dimensional sparse covariance matrices , 2008, 0901.3220.
[19] A. Willsky,et al. Latent variable graphical model selection via convex optimization , 2010 .
[20] Yonina C. Eldar,et al. GESPAR: Efficient Phase Retrieval of Sparse Signals , 2013, IEEE Transactions on Signal Processing.
[21] M. Rudelson,et al. Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements , 2006, 2006 40th Annual Conference on Information Sciences and Systems.
[22] E. Candès. The restricted isometry property and its implications for compressed sensing , 2008 .
[23] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[24] Pablo A. Parrilo,et al. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..
[25] Christos Thrampoulidis,et al. Regularized Linear Regression: A Precise Analysis of the Estimation Error , 2015, COLT.
[26] Yue Sun,et al. Low-Rank Positive Semidefinite Matrix Recovery From Corrupted Rank-One Measurements , 2016, IEEE Transactions on Signal Processing.
[27] E. Candès,et al. Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.
[28] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2009, Found. Comput. Math..
[29] Y. Gordon. Some inequalities for Gaussian processes and applications , 1985 .
[30] David L. Donoho,et al. Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[31] Justin K. Romberg,et al. Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.
[32] Emmanuel J. Candès,et al. PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming , 2011, ArXiv.
[33] Anru Zhang,et al. ROP: Matrix Recovery via Rank-One Projections , 2013, ArXiv.
[34] Y. Gordon. On Milman's inequality and random subspaces which escape through a mesh in ℝ n , 1988 .
[35] Babak Hassibi,et al. New Null Space Results and Recovery Thresholds for Matrix Rank Minimization , 2010, ArXiv.