1 |
Bardsley J M , Nagy J G . Covariance-preconditioned iterative methods for nonnegatively constrained astronomical imaging[J]. SIAM Journal on Matrix Analysis and Applications, 2006, 27 (4): 1184- 1197.
doi: 10.1137/040615043
|
2 |
Bruckstein A M , Elad M , Zibulevsky M . On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations[J]. IEEE Transactions on Information Theory, 2008, 54 (11): 4813- 4820.
doi: 10.1109/TIT.2008.929920
|
3 |
Donoho D L, Tanner J. Sparse nonnegative solution of underdetermined linear equations by linear programming[C]//Proceedings of the National Academy of Sciences of the United States of America, 2005: 9446-9451.
|
4 |
Donoho D L , Tanner J . Counting the faces of randomly-projected hypercubes and orthants, with applications[J]. Discrete and Computational Geometry, 2010, 43 (3)
|
5 |
Khajehnejad M A , Dimakis A G , Xu W Y , et al. Sparse recovery of nonnegative signals with minimal expansion[J]. IEEE Transactions on Signal Processing, 2010, 59 (1): 196- 208.
|
6 |
O'Grady P D, Rickard S T. Recovery of non-negative Signals from compressively sampled observations via non-negative quadratic programming[C]//SPARS'09-Signal Processing with Adaptive Sparse Structured Representations, 2009.
|
7 |
Vo N, Moran B, Challa S. Nonnegative-least-square classifier for face recognition[C]//Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks, 2009: 449-456.
|
8 |
Wang M , Xu W Y , Tang A . A unique nonnegative solution to an underdetermined system: from vectors to matrices[J]. IEEE Transactions on Signal Processing, 2011, 59 (3): 1007- 1016.
doi: 10.1109/TSP.2010.2089624
|
9 |
Bradley P S , Fayyad U M , Mangasarian O L . Mathematical programming for data mining: formulations and challenges[J]. INFORMS Journal on Computing, 1999, 11 (3): 217- 238.
doi: 10.1287/ijoc.11.3.217
|
10 |
Bradley P S , Mangasarian O L , Rosen J B . Parsimonious least norm approximation[J]. Computational Optimization and Applications, 1998, 11 (1): 5- 21.
doi: 10.1023/A:1018361916442
|
11 |
He R, Zheng W S, Hu B, et al. Nonnegative sparse coding for discriminative semi-supervised learning[C]//2011 IEEE Conference on Computer Vision and Pattern Recognition, 2011: 2849-2856.
|
12 |
Mangasarian O L . Machine learning via polyhedral concave minimization[J]. Applied Mathematics and Parallel Computing, 1996, 175- 188.
|
13 |
Szlam A, Guo Z H, Osher S. A split Bregman method for non-negative sparsity penalized least squares with applications to hyperspectral demixing[C]//IEEE International Conference on Imaging Processing, 2010: 1917-1920.
|
14 |
Candès E J , Wakin M B , Boyd S P . Enhancing sparsity by reweighted l1 minimization[J]. Journal of Fourier Analysis and Applications, 2008, 14 (5): 877- 905.
|
15 |
Zhao Y B , Li D . Reweighted l1-minimization for sparse solutions to underdetermined linear systems[J]. SIAM Journal on Optimization, 2012, 22 (3): 1065- 1088.
doi: 10.1137/110847445
|
16 |
Zhao Y B . Equivalence and strong equivalence between sparsest and least l1-norm nonnegative solutions of linear systems and their application[J]. Journal of the Operations Research Society of China, 2014, 2 (2): 171- 194.
doi: 10.1007/s40305-014-0043-1
|
17 |
Gao Y , Peng J G , Yue S G , et al. On the null space property of lq-minimization for 0< q ≤ 1 in compressed sensing[J]. Journal of Function Spaces, 2015, 1- 10.
|
18 |
Foucart S , Rauhut H . A Mathematical Introduction to Compressive Sensing[M]. New York: Springer Science+Business Media, 2013: 1- 39.
|
19 |
Qin L X , Xiu N H , Kong L C , et al. Linear program relaxation of sparse nonnegative recovery in compressive sensing microarrays[J]. Computational and Mathematical Methods in Medicine, 2012, 646045.
|
20 |
Juditsky A , Nemirovski A S . On verifiable sufficient conditions for sparse signal recovery via l1 minimization[J]. Mathematical Programming, 2011, 127 (1): 57- 88.
doi: 10.1007/s10107-010-0417-z
|