[1] |
Donoho, D. L., Compressed sensing, IEEE Trans. Inf. Theory, 52, 1289-1306 (2006) · Zbl 1288.94016 · doi:10.1109/TIT.2006.871582 |
[2] |
Candès, E. J.; Romberg, J.; Tao, T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inf. Theory, 52, 489-509 (2006) · Zbl 1231.94017 · doi:10.1109/TIT.2005.862083 |
[3] |
Candès, E. J.; Romberg, J.; Tao, T., Stable signal recovery from incomplete and inaccurate measurements, Commun. Pure Appl. Math., 59, 1207-1223 (2006) · Zbl 1098.94009 · doi:10.1002/cpa.20124 |
[4] |
Candès, E. J., The restricted isometry property and its implications for compressed sensing, C. R. Acad. Sci., Sér., 346, 589-592 (2008) · Zbl 1153.94002 |
[5] |
Chen, S.; Donoho, D.; Saunders, M. A., Atomic decomposition by basis pursuit, SIAM J. Sci. Comput., 20, 33-61 (1998) · Zbl 0919.94002 · doi:10.1137/S1064827596304010 |
[6] |
Natarajan, B. K., Sparse approximate solutions to linear systems, SIAM J. Sci. Comput., 24, 227-234 (1995) · Zbl 0827.68054 · doi:10.1137/S0097539792240406 |
[7] |
Gribnoval, R.; Nielsen, M., Sparse representations in unions of bases, IEEE Trans. Inf. Theory, 49, 3320-3325 (2003) · Zbl 1286.94032 · doi:10.1109/TIT.2003.820031 |
[8] |
Majumdar, A.; Ward, R., Compressed sensing of color images, Signal Process., 90, 12, 3122-3127 (2010) · Zbl 1197.94089 · doi:10.1016/j.sigpro.2010.05.016 |
[9] |
Malioutov, D.; Cetin, M.; Willsky, A., Sparse signal reconstruction perspective for source localization with sensor arrays, IEEE Trans. Signal Process., 53, 8, 3010-3022 (2005) · Zbl 1370.94191 · doi:10.1109/TSP.2005.850882 |
[10] |
Parvaresh, F.; Vikalo, H.; Misra, S.; Hassibi, B., Recovering sparse signals using sparse measurement matrices in compressed DNA microarrays, IEEE J. Sel. Top. Signal Process., 2, 3, 275-285 (2008) · doi:10.1109/JSTSP.2008.924384 |
[11] |
Mishali, M.; Eldar, Y. C., Reduce and boost: recovering arbitrary sets of jointly sparse vectors, IEEE Trans. Signal Process., 56, 10, 4692-4702 (2008) · Zbl 1390.94306 · doi:10.1109/TSP.2008.927802 |
[12] |
Vidal, R.; Ma, Y., A unified algebraic approach to 2-D and 3-D motion segmentation and estimation, J. Math. Imaging Vis., 25, 3, 403-421 (2005) · Zbl 1478.68411 · doi:10.1007/s10851-006-8286-z |
[13] |
Erickson, S.; Sabatti, C., Empirical Bayes estimation of a sparse vector of gene expression changes, Stat. Appl. Genet. Mol. Biol., 4 (2005) · Zbl 1081.62093 · doi:10.2202/1544-6115.1132 |
[14] |
Cotter, S.; Rao, B., Sparse channel estimation via matching pursuit with application to equalization, IEEE Trans. Commun., 50, 3, 374-377 (2002) · doi:10.1109/26.990897 |
[15] |
Huang, S.; Yang, Y.; Yang, D., Class specific sparse representation for classification, Signal Process., 116, 38-42 (2015) · doi:10.1016/j.sigpro.2015.04.018 |
[16] |
Liu, Y.; Wan, Q., Enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access, EURASIP J. Adv. Signal Process., 2012, 177, 1-11 (2012) |
[17] |
Chen, W.; Li, Y., The high order RIP condition for signal recovery, J. Comput. Math., 37, 1, 61-75 (2019) · Zbl 1438.94033 · doi:10.4208/jcm.1710-m2017-0175 |
[18] |
Gao, Y.; Ma, M., A new bound on the block restricted isometry constant in compressed sensing, J. Inequal. Appl., 2017 (2017) · Zbl 1380.46008 · doi:10.1186/s13660-017-1448-2 |
[19] |
Wang, Y.; Wang, J.; Xu, Z., Restricted p-isometry properties of nonconvex blocksparse compressed sensing, Signal Process., 104, 188-196 (2014) · doi:10.1016/j.sigpro.2014.03.040 |
[20] |
Cai, Y.: Weighted \(l_p-l_1\) minimization methods for block sparse recovery and rank minimization. Anal. Appl. (2020) |
[21] |
Wen, J.; Zhou, Z.; Liu, Z.; Lai, M.; Tang, X., Sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit, Appl. Comput. Harmon. Anal., 47, 3, 948-974 (2019) · Zbl 1422.94017 · doi:10.1016/j.acha.2018.02.002 |
[22] |
Lin, L.; Li, S., Block sparse recovery via mixed l2/l1 minimization, Acta Math. Sin., 29, 7, 1401-1412 (2013) · Zbl 1322.94034 · doi:10.1007/s10114-013-1564-y |
[23] |
Candès, E. J.; Tao, T., Decoding by linear programming, IEEE Trans. Inf. Theory, 51, 12, 4203-4215 (2005) · Zbl 1264.94121 · doi:10.1109/TIT.2005.858979 |
[24] |
Eldar, Y.; Mishali, M., Robust recovery of signals from a structured union of subspaces, IEEE Trans. Inf. Theory, 55, 11, 5302-5316 (2009) · Zbl 1367.94087 · doi:10.1109/TIT.2009.2030471 |
[25] |
Tomasi, C.; Kanade, T., Shape and motion from image streams under orthography: a factorization method, Int. J. Comput. Vis., 9, 1, 137-154 (1992) · doi:10.1007/BF00129684 |
[26] |
Basri, R.; Jacobs, D., Lambertian reflectance and linear subspaces, IEEE Trans. Pattern Anal. Mach. Intell., 25, 2, 218-233 (2003) · doi:10.1109/TPAMI.2003.1177153 |
[27] |
Abernethy, J.; Bach, F.; Evgeniou, T.; Vert, J. P., A new approach to collaborative filtering: operator estimation with spectral regularization, J. Mach. Learn. Res., 10, 803-826 (2009) · Zbl 1235.68122 |
[28] |
Rennie, J. D.M.; Srebro, N., Fast maximum margin matrix factorization for collaborative prediction, Proc. Int. Conf. Mach. Learn., 713-719 (2005) |
[29] |
Mesbahi, M.; Papavassilopoulos, G. P., On the rank minimization problem over a positive semidefinite linear matrix inequality, IEEE Trans. Autom. Control, 42, 2, 239-243 (1997) · Zbl 0872.93029 · doi:10.1109/9.554402 |
[30] |
Srebro, N.: Learning with matrix factorizations. Ph.D. dissertation, Massachusetts Inst. Technol, Cambridge, MA, USA (2004) |
[31] |
Gross, D.; Liu, Y. K.; Flammia, S. T.; Becker, S.; Eisert, J., Quantum state tomography via compressed sensing, Phys. Rev. Lett., 105 (2010) · doi:10.1103/PhysRevLett.105.150401 |
[32] |
Liu, Z.; Vandenberghe, L., Interior-point method for nuclear norm approximation with application to system identification, SIAM J. Matrix Anal. Appl., 31, 3, 1235-1256 (2009) · Zbl 1201.90151 · doi:10.1137/090755436 |
[33] |
Amit, Y., Fink, M., Srebro, N., Ullman, S.: Uncovering shared structures in multiclass classification. In: Proc. 24th Int. Conf. Mach. Learn., pp. 17-24. (2007) |
[34] |
Morita, T.; Kanade, T., A sequential factorization method for recovering shape and motion from image streams, IEEE Trans. Pattern Anal. Mach. Intell., 19, 8, 858-867 (1997) · doi:10.1109/34.608289 |
[35] |
Zhang, M.; Huang, Z. H.; Zhang, Y., Restricted p-isometry properties of nonconvex matrix recovery, IEEE Trans. Inf. Theory, 59, 7, 4316-4323 (2013) · Zbl 1364.94179 · doi:10.1109/TIT.2013.2250577 |
[36] |
Ma, T. H.; Lou, Y.; Huang, T. Z., Truncated \(l_{1-2}\) models for sparse recovery and rank minimization, SIAM J. Imaging Sci., 10, 3, 1346-1380 (2017) · Zbl 1397.94021 · doi:10.1137/16M1098929 |
[37] |
Candès, E. J.; Tao, T., The power of convex relaxation: near-optimal matrix completion, IEEE Trans. Inf. Theory, 56, 5, 2053-2080 (2010) · Zbl 1366.15021 · doi:10.1109/TIT.2010.2044061 |
[38] |
Fazel, M.; Hindi, H.; Boyd, S., A rank minimization heuristic with application to minimum order system approximation, Proc. IEEE Amer. Control Conf, 4734-4739 (2001) |
[39] |
Candès, E. J.; Plan, Y., Tight oracle bounds for low-rank recovery from a minimal number of random measurements, IEEE Trans. Inf. Theory, 57, 4, 2342-2359 (2011) · Zbl 1366.90160 · doi:10.1109/TIT.2011.2111771 |
[40] |
Kong, L.C., Xiu, N.H.: Exact low-rank matrix recovery via non-convex \(M_p\)-minimization, Optimization. - Online (2011) |
[41] |
Recht, B.; Fazel, M.; Parrilo, P. A., Guaranteed minimum rank solutions of linear matrix equations via nuclear norm minimization, SIAM Rev., 52, 2, 471-501 (2010) · Zbl 1198.90321 · doi:10.1137/070697835 |
[42] |
Esser, E.; Lou, Y.; Xin, J., A method for finding structured sparse solutions to nonnegative least squares problems with applications, SIAM J. Imaging Sci., 6, 2010-2046 (2013) · Zbl 1282.90239 · doi:10.1137/13090540X |
[43] |
Yin, P.; Lou, Y.; He, Q.; Xin, J., Minimization of \(l_{1-2}\) for compressed sensing, SIAM J. Sci. Comput., 37, 536-563 (2015) · Zbl 1316.90037 · doi:10.1137/140952363 |
[44] |
Wang, D.; Zhang, Z., Generalized sparse recovery model and its neural dynamical optimization method for compressed sensing, Circuits Syst. Signal Process., 36, 4326-4353 (2017) · Zbl 1373.94725 · doi:10.1007/s00034-017-0532-7 |
[45] |
Zhao, Y.; He, X.; Huang, T.; Huang, J., Smoothing inertial projection neural network for minimization \(L_{p-q}\) in sparse signal reconstruction, Neural Netw., 99, 33-41 (2018) · Zbl 1456.94024 · doi:10.1016/j.neunet.2017.12.008 |