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[1] L. Santaló. Geometría integral en espacios de curvatura constante , 1952 .
[2] V. Marčenko,et al. DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES , 1967 .
[3] B. Grünbaum. Grassmann angles of convex polytopes , 1968 .
[4] J. Claerbout,et al. Robust Modeling With Erratic Data , 1973 .
[5] P Mcmullen,et al. Non-linear angle-sum relations for polyhedral cones and polytopes , 1975, Mathematical Proceedings of the Cambridge Philosophical Society.
[6] W. Boothby. An introduction to differentiable manifolds and Riemannian geometry , 1975 .
[7] H. Hadwiger. Gitterpunktanzahl im Simplex und Wills'sche Vermutung , 1979 .
[8] S. Geman. A Limit Theorem for the Norm of Random Matrices , 1980 .
[9] R. Muirhead. Aspects of Multivariate Statistical Theory , 1982, Wiley Series in Probability and Statistics.
[10] J. W. Silverstein. The Smallest Eigenvalue of a Large Dimensional Wishart Matrix , 1985 .
[11] Rolf Schneider,et al. Random projections of regular simplices , 1992, Discret. Comput. Geom..
[12] J. K. Böröczky,et al. Random projections of regular polytopes , 1999 .
[13] Arkadi Nemirovski,et al. On sparse representation in pairs of bases , 2003, IEEE Trans. Inf. Theory.
[14] E. Candès,et al. Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.
[15] Emmanuel J. Candès,et al. Decoding by linear programming , 2005, IEEE Transactions on Information Theory.
[16] 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.
[17] M. Rudelson,et al. Geometric approach to error-correcting codes and reconstruction of signals , 2005, math/0502299.
[18] Robert D. Nowak,et al. Signal Reconstruction From Noisy Random Projections , 2006, IEEE Transactions on Information Theory.
[19] Nathan Linial,et al. How Neighborly Can a Centrally Symmetric Polytope Be? , 2006, Discret. Comput. Geom..
[20] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[21] E.J. Candes. Compressive Sampling , 2022 .
[22] Martin J. Wainwright,et al. Sharp thresholds for high-dimensional and noisy recovery of sparsity , 2006, ArXiv.
[23] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[24] David L. Donoho,et al. High-Dimensional Centrally Symmetric Polytopes with Neighborliness Proportional to Dimension , 2006, Discret. Comput. Geom..
[25] Richard G. Baraniuk,et al. Random Filters for Compressive Sampling and Reconstruction , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[26] Борис Сергеевич Кашин,et al. Замечание о задаче сжатого измерения@@@A Remark on Compressed Sensing , 2007 .
[27] R. DeVore,et al. Compressed sensing and best k-term approximation , 2008 .
[28] Weiyu Xu,et al. Compressed sensing of approximately sparse signals , 2008, 2008 IEEE International Symposium on Information Theory.
[29] B. Hassibi,et al. Compressed sensing over the Grassmann manifold: A unified analytical framework , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.
[30] R. DeVore,et al. A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .
[31] Weiyu Xu,et al. Compressed sensing - probabilistic analysis of a null-space characterization , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[32] Mihailo Stojnic,et al. Various thresholds for ℓ1-optimization in compressed sensing , 2009, ArXiv.
[33] S. Vavasis. Derivation of Compressive Sensing Theorems from the Spherical Section property , 2009 .
[34] Weiyu Xu,et al. Weighted ℓ1 minimization for sparse recovery with prior information , 2009, 2009 IEEE International Symposium on Information Theory.
[35] Jeffrey D. Blanchard,et al. THE RESTRICTED ISOMETRY PROPERTY AND ` Q-REGULARIZATION : PHASE TRANSITIONS FOR SPARSE APPROXIMATION , 2009 .
[36] 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.
[37] Weiyu Xu,et al. Breaking through the thresholds: an analysis for iterative reweighted ℓ1 minimization via the Grassmann angle framework , 2009, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[38] Andrea Montanari,et al. The Noise-Sensitivity Phase Transition in Compressed Sensing , 2010, IEEE Transactions on Information Theory.