IMPROVED BOUNDS ON RESTRICTED ISOMETRY CONSTANTS FOR GAUSSIAN MATRICES

被引:47
作者
Bah, Bubacarr [1 ,2 ]
Tanner, Jared [1 ,2 ]
机构
[1] Univ Edinburgh, Maxwell Inst, Edinburgh EH9 3JZ, Midlothian, Scotland
[2] Univ Edinburgh, Sch Math, Edinburgh EH9 3JZ, Midlothian, Scotland
关键词
Wishart matrices; compressed sensing; sparse approximation; restricted isometry constant; phase transitions; Gaussian matrices; singular values of random matrices; RECONSTRUCTION;
D O I
10.1137/100788884
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The restricted isometry constant ( RIC) of a matrix A measures how close to an isometry is the action of A on vectors with few nonzero entries, measured in the l(2) norm. Specifically, the upper and lower RICs of a matrix A of size nxN are the maximum and the minimum deviation from unity (one) of the largest and smallest, respectively, square of singular values of all ((N)(k)) matrices formed by taking k columns from A. Calculation of the RIC is intractable for most matrices due to its combinatorial nature; however, many random matrices typically have bounded RIC in some range of problem sizes (k, n, N). We provide the best known bound on the RIC for Gaussian matrices, which is also the smallest known bound on the RIC for any large rectangular matrix. Our results are built on the prior bounds of Blanchard, Cartis, and Tanner [SIAM Rev., to appear], with improvements achieved by grouping submatrices that share a substantial number of columns.
引用
收藏
页码:2882 / 2898
页数:17
相关论文
共 24 条
[1]  
[Anonymous], 2001, MATH SURVEYS MONOGR
[2]  
[Anonymous], 2010, Theoretical foundations and numerical methods for sparse recovery, DOI DOI 10.1515/9783110226157.1
[3]   Decay properties of restricted isometry constants [J].
Blanchard, Jeffey D. ;
Cartis, Coralia ;
Tanner, Jared .
IEEE Signal Processing Letters, 2009, 16 (07) :572-575
[4]  
Blanchard J. D., APPL COMPUT IN PRESS
[5]  
BLANCHARD JD, SIAM REV IN PRESS
[6]   Iterative hard thresholding for compressed sensing [J].
Blumensath, Thomas ;
Davies, Mike E. .
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2009, 27 (03) :265-274
[7]   Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information [J].
Candès, EJ ;
Romberg, J ;
Tao, T .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (02) :489-509
[8]   Decoding by linear programming [J].
Candes, EJ ;
Tao, T .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2005, 51 (12) :4203-4215
[9]   The restricted isometry property and its implications for compressed sensing [J].
Candes, Emmanuel J. .
COMPTES RENDUS MATHEMATIQUE, 2008, 346 (9-10) :589-592
[10]  
d'Aspremont A, 2008, J MACH LEARN RES, V9, P1269