[1] OLSHAUSEN B, FIELD D. Sparse coding with an overcomplete basis set: a strategy employed by V1[J]. Vision Research, 1997,37(23):3311-3325. [2] TIBSHIRANI R. Regression shrinkage and selection via the LASSO[J]. Journal of the Royal Statistical Society B, 1996 58(1):267-288. [3] DONOHO D. For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution[J]. Communications on Pure and Applied Mathematics, 2006,59(6):797-829. [4] TROPP J A, WRIGHT S J. Computational methods for sparse solution of linear inverse problems[J]. Proceedings of the IEEE, 2010,98(6):948-958. [5] AHARON M, ELAD M, BRUCKSTEIN A M. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006,54(11):4311-4322. [6] MAIRAL J, BACH F, PONCE J. Non-local sparse models for image restoration[C]//Proceedings of 12th International Conference on Computer Vision. Piscataway: IEEE, 2009:2272-2279. [7] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009,31(2):210-227. [8] YANG J, YU K, GONG Y, et al. Linear spatial pyramid matching using sparse coding for image classification[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009:1794-1801. [9] GAO S, TSANG I W-H, CHIA L-T. Kernel sparse representation for image classification and face recognition[C]//Proceedings of the 11th European Conference on Computer Vision. Berlin: Springer, 2010:1-14. [10] YANG M, ZHANG L. Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary[C]//Proceedings of the 11th European Conference on Computer Vision. Berlin: Springer, 2010:448-461. [11] WRIGHT J, MA Y, MAIRAL J. Sparse representation for computer vision and pattern recognition[J]. Proceedings of IEEE, 2010,98(6):1031-1044. [12] CANDES E, RECHT B. Exact low-rank matrix completion via convex optimization[C]//Proceedings of the 46th Annual Allerton Conference on Communication, Control, and Computing. Piscataway: IEEE, 2008:806-812. [13] WRIGHT J, GANESH A, RAO S, et al. Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization[C]//Proceedings of the Conference on Neural Information Processing Systems. Vancouver: Curran Associates incorporated, 2009:2080-2088. [14] LIN Z, GANESH A, WRIGHT J, et al. Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix[EB/OL].[2014-08-26]. http://yima.csl.illinois.edu/psfile/rpca_algorithms.pdf. [15] BERTSEKAS D. Nonlinear programming[M]. Nashua: Athena Scientific Press, 2004:357-359. [16] ZHANG L, YANG M, FENG X. Sparse representation or collaborative representation: which helps face recognition[C]//Proceedings of the IEEE 13th International Conference on Computer Vision. Piscataway: IEEE, 2011:471-478. [17] SHI Q, ERIKSSON A, van den HENGEL A, et al. Is face recognition really a compressive sensing problem[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2011:553-560. [18] RIGAMONTI R, BROWN M A, LEPETIT V. Are sparse representations really relevant for image classification[C]//Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2011:1545-1552. |