Donoho D L. High-dimensional data analysis: the curses and blessings of dimensionality. American Mathematical Society Math Challenges Lecture, 2000. 1-32
[2]
Candès E J, Tao T. Decoding by linear programming. IEEE Transactions on Information Theory, 2004, 51(12): 4203- 4215
[3]
Li Shu-Tao, Wei Dan. A survey on compressive sensing. Acta Automatica Sinica, 2009, 35(11): 1369-1377 (李树涛, 魏丹. 压缩传感综述. 自动化学报, 2009, 35(11): 1369- 1377)
[4]
Dai Qiong-Hai, Fu Chang-Jun, Ji Xiang-Yang. Research on compressed sensing. Chinese Journal of Computers, 2011, 34(3): 425-434 (戴琼海, 付长军, 季向阳. 压缩感知研究. 计算机学报, 2011, 34(3): 425-434)
[5]
Ma Jian-Wei, Xu Jie, Bao Yue-Quan, Yu Si-Wei. Compressive sensing and its application: from sparse to low-rank regularized optimization. Signal Processing, 2012, 28(5): 609- 623 (马坚伟, 徐杰, 鲍跃全, 于四伟. 压缩感知及其应用: 从稀疏约束到低秩约束优化. 信号处理, 2012, 28(5): 609-623)
[6]
Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306
[7]
SIGKDD A, KDD Cup N, Workshop [Online], available: http://www.cs.uic.edu/～liub/KDD-cup-2007/proceedings. html, October 31, 2012
[8]
Candès E J, Li X D, Ma Y, Wright J. Robust principal component analysis? Journal of the ACM, 2011, 58(3): 1-37
[9]
Chen Min-Ming. Algorithms and Implementation of Matrix Reconstruction [Master dissertation], Institute of Computing Technology of Chinese Academy of Science, China, 2010 (陈敏铭. 矩阵重建的算法与实现 [硕士学位论文], 中国科学院计算机技术研究所, 中国, 2010)
[10]
Ganesh A, Wright J, Li X D, Candès E J, Ma Y. Dense error correction for low-rank matrices via principal component pursuit. In: Proceedings of the 2010 IEEE International Symposium on Information Theory. Austin, TX: IEEE, 2010. 1513-1517
[11]
Cai J F, Candès E J, Shen Z W. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 2010, 20(4): 1956-1982
[12]
Lin Z C, Chen M M, Wu L Q, Ma Y. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-rank Matrices. UIUC Technical Report UILU-ENG-09-2215, arXiv preprint arXiv: 1009.5055, 2010
[13]
Yang A, Ganesh A, Sastry S, Ma Y. Fast l_1-minimization algorithms and an application in robust face recognition: a review. In: Proceedings of the 2010 IEEE International Conference on Image Processing, 2010. 1849-1852
[14]
Fornasier M, Rauhut H, Ward R. Low-rank matrix recovery via iteratively reweighted least squares minimization. SIAM Journal on Optimization, 2011, 21(4): 1614-1640
[15]
He R, Sun Z N, Tan T N, Zheng W S. Recovery of corrupted low-rank matrices via half-quadratic based nonconvex minimization. In: Proceedings of the 2011 IEEE International Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2011. 2889-2896
[16]
Lustig M, Donoho D, Pauly J M. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine, 2007, 58(6): 1182-1195
[17]
Lustig M, Donoho D L, Santos J M, Pauly J M. Compressed sensing MRI. IEEE Signal Processing Magazine, 2008, 25(2): 72-82
[18]
Duarte M F, Davenport M A, Takhar D, Laska J N, Sun T, Kelly K F, Baraniuk R G. Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2): 83-91
[19]
Wright J, Yang A Y, Ganesh A, Sastry S S, Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227
[20]
Wagner A, Wright J, Ganesh A, Zhou Z H, Ma Y. Towards a practical face recognition system: robust registration and illumination by sparse representation. In: Proceedings of the 2009 IEEE International Conference on Computer Vision and Pattern Recognition. Miami, FL: IEEE, 2009. 597-604
[21]
Zhou Z, Wagner A, Mobahi H, Wright J, Ma Y. Face recognition with contiguous occlusion using Markov random fields. In: Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto: IEEE, 2009. 1050-1057
[22]
Ma L, Wang C C, Xiao B H, Zhou W. Sparse representation for face recognition based on discriminative low-rank dictionary learning. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012. 2586-2593
[23]
Yang J C, Wright J, Huang T S, Ma Y. Image super-resolution as sparse representation of raw image patches. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK: IEEE, 2008. 1-8
[24]
Veeraraghavan A, Reddy D, Raskar R. Coded strobing photography: compressive sensing of high speed periodic events. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(4): 671-686
[25]
Peers P, Mahajan D K, Lamond B, Ghosh A, Matusik W, Ramamoorthi R, Debevec P. Compressive light transport sensing. ACM Transactions on Graphics, 2009, 28(1): 1-18
[26]
Huang F C, Ramamoorthi R. Sparsely precomputing the light transport matrix for real-time rendering. Computer Graphics Forum, 2010, 29(4): 1335-1345
[27]
Karbasi A, Oh S, Parhizkar R, Vetterli M. Ultrasound tomography calibration using structured matrix completion. In: Proceedings of the 20th International Congress on Acoustics. Sydney, Australia, 2010
[28]
Parhizkar R, Karbasi A, Oh S, Vetterli M. Calibration using matrix completion with application to ultrasound tomography [Online], available: http://arxiv.org/abs/1012.4928, July 5, 2013
[29]
Wu L, Ganesh A, Shi B, Matsushita Y, Wang Y T, Ma Y. Robust photometric stereo via low-rank matrix completion and recovery. In: Proceedings of the 10th Asian Conference on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2010. 703-717
[30]
Wu L, Ganesh A, Shi B, Matsushita Y, Wang Y T, Ma Y. Convex optimization based low-rank matrix completion and recovery for photometric stereo and factor classification [Online], available: http://perception.csl.illinois.edu/matrix-rank/stereo.html, June 25, 2013
[31]
Peng Y G, Ganesh A, Wright J, Xu W L, Ma Y. RASL: robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2012, 34(11). 2233-2246
[32]
Wu K K, Wang L J, Soong F K, Yam Y. A sparse and low-rank approach to efficient face alignment for photo-real talking head synthesis. In: Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing. Prague: IEEE, 2011. 1397-1400
[33]
Zhang Z D, Liang X, Ganesh A, Ma Y. TILT: transform invariant low-rank textures. In: Proceedings of the 2011 Computer Vision -- ACCV, Springer Berlin Heidelberg, 2011. 314 -328
[34]
Zhang Z D, Liang X, Ma Y. Unwrapping low-rank textures on generalized cylindrical surfaces. In: Proceedings of the 2011 International Conference on Computer Vision (ICCV). Barcelona, Spain: IEEE, 2011. 1347-1354
[35]
Zhang Z D, Matsushita Y, Ma Y. Camera calibration with lens distortion from low-rank textures. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI: IEEE, 2011. 2321- 2328
[36]
Zhang X, Lin Z, Sun F, Ma Y. Rectification of Optical Characters as Transform Invariant Low-rank Textures. International Conference on Document Analysis and Recognition (ICDAR), 2013
[37]
Garg K, Nayar S K. Detection and removal of rain from videos. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Washington, DC, USA: IEEE, 2004. I-528-I-535
[38]
Eldar Y C, Mishali M. Robust recovery of signals from a structured union of subspaces. IEEE Transactions on Information Theory, 2009, 55(11): 5302-5316
[39]
Mackey L W, Talwalkar A, Jordan M I. Divide-and-conquer matrix factorization. In: Proceedings of the 2011 Neural Information Processing Systems, 2011. 1134-1142
[40]
Drineas P, Kannan R, Mahoney M W. Fast Monte Carlo algorithms for matrices II: computing a low-rank approximation to a matrix. SIAM Journal on Optimization, 2006, 36(1): 158-183
[41]
Zhuang L S, Gao H Y, Lin Z C, Ma Y, Zhang X, Yu N H. Non-negative low rank and sparse graph for semi-supervised learning. In: Proceedings of the 2012 Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012. 2328-2335
[42]
Shen X, Wu Y. A unified approach to salient object detection via low rank matrix recovery. In: Proceedings of the 2012 Computer Vision and Pattern Recognition, 2012. 853-860
[43]
Ye G N, Liu D, Jhuo I H, Chang S F. Robust late fusion with rank minimization. In: Proceedings of the 2012 Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012. 3021-3028
[44]
Natarajan B K. Sparse approximate solutions to linear systems. SIAM Journal of Computing, 1995, 24(2): 227-234
[45]
Recht B, Fazel M, Parrilo P A. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Review, 2010, 52(3): 471-501
[46]
Candès E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489-509
[47]
Special section compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2): 12-101
[48]
Yang J Y, Peng Y G, Xu W L, Dai Q H. Ways to sparse representation: an overview. Science in China Series F: Information Sciences, 2009, 52(4): 695-703
[49]
Elad M. Sparse and Redundant Representations: from Theory to Applications in Signal and Image Processing. New York: Springer, 2010
[50]
Eldar Y C, Kutyniok G. Compressed Sensing: Theory and Applications. Cambridge: Cambridge University Press, 2012
[51]
Taubman D S, Marcellin M W. JPEG 2000: Image Compression Fundamentals, Standards and Practice. The International Series in Engineering and Computer Science. New Yoik: Springer, 2002
[52]
Candès E J, Tao T. The power of convex relaxation: near-optimal matrix completion. IEEE Transactions on Information Theory, 2009, 56(5): 2053-2080
[53]
Chandrasekaran V, Sanghavi S, Parrilo P A, Willsky A S. Rank-sparsity incoherence for matrix decomposition. SIAM Journal on Optimization, 2011, 21(2): 572-596
[54]
Valiant L G. Graph-theoretic arguments in low-level complexity. In: Proceedings of the 6th Symposium on Mathematical Foundations of Computer Science. New York: Springer, 1977. 162-176
[55]
Zhou Z H, Li X D, Wright J, Candès E J, Ma Y. Stable principal component pursuit. In: Proceedings of the 2010 IEEE International Symposium on Information Theory. Austin, TX: IEEE, 2010. 1518-1522
[56]
Combettes P L, Wajs V R. Signal recovery by proximal forward-backward splitting. SIAM Multiscale Modeling and Simulation, 2005, 4(4): 1168-1200
[57]
Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problem. SIAM Journal on Imaging Sciences, 2008, 2(1) 183-202
[58]
Toh K C, Yun S. An accelerated proximal gradient algorithm for nuclear norm regularized least squares problems. Pacific Journal of Optimization, 2010, 6(3): 615-640
[59]
Yuan X M, Yang J F. Sparse and low-rank matrix decomposition via alternating direction methods [Online], available: http://www.math.hkbu.edu.hk/～xmyuan/Publication.html, June 25, 2013
[60]
Yin W, Osher S, Goldfarb D, Darbon J. Bregman iterative algorithms for l_1-minimization with applications to compressed sensing. SIAM Journal on Imaging Sciences, 2008, 1(1): 143-168
[61]
Rockafellar R T. Convex Analysis. Princeton: Princeton University Press, 1970
[62]
Daubechies I, Defrise M, de Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications in Pure and Applied Mathematics, 2004, 57(11): 1413-1457
[63]
Daubechies I, DeVore R, Fornasier M, Güntürk S. Iteratively reweighted least squares minimization for sparse recovery. Communications on Pure and Applied Mathematics, 2010, 63(1): 1-38
[64]
Studer V, Bobin J, Chahid M, Mousavi S H S, Candès E, Dahan M. Compressive fluorescence microscopy for biological and hyperspectral imaging. Proceedings of the National Academy of Sciences of the United States of America, 2011, 109(26): E1679-E1687
[65]
Wagner A, Wright J, Ganesh A, Zhou Z, Mobahi H, Ma Y. Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(2): 372-386
[66]
Kroeker K L. Face recognition breakthrough. ACM Communication, 2009, 52(8): 18-19
[67]
Yang M, Zhang L. Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: Proceedings of the 11th European Conference on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2010. 448-461
[68]
Chen C F, Wei C P, Wang Y C F. Low-rank matrix recovery with structural incoherence for robust face recognition. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012. 2618-2625
[69]
Yang J C, Wright J, Huang T S, Ma Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873
[70]
Debevec P, Hawkins T, Tchou C, Duiker H P, Sarokin W, Sagar M. Acquiring the reflectance field of a human face. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques. New York, USA: ACM, 2000. 146-156
[71]
Wang J P, Dong Y, Tong X, Lin Z C, Guo B N. Kernel Nystr？m method for light transport. Proceedings of ACM SIGGRAPH, 2009, 28(3): 1-10
[72]
Ji H, Liu C Q, Shen Z W, Xu Y H. Robust video denoising using low rank matrix completion. In: Proceedings of the 2010 IEEE International Conference on Computer Vision and Pattern Recognition. San Francisco, CA: IEEE, 2010. 1791-1798
[73]
Ji H, Huang S B, Shen Z W, Xu Y H. Robust video restoration by joint sparse and low rank matrix approximation. SIAM Journal on Imaging Sciences, 2011, 4(4): 1122-1142
[74]
Parhizkar R, Karbasi A, Vetterli M. Calibration in circular ultrasound tomography devices. In: Proceedings of the 36th International Conference on Acoustics, Speech and Signal Processing. Prague: IEEE, 2011. 549-552
[75]
Keshavan R H, Montanari A, Oh S. Matrix completion from a few entries. IEEE Transactions on Information Theory, 2010, 56(6): 2980-2998
[76]
Peng Y G, Ganesh A, Wright J, Xu W L, Ma Y. RASL: robust alignment by sparse and low-rank decomposition for linearly correlated images. In: Proceedings of the 2010 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA: IEEE, 2010. 763-770
[77]
Tan W T, Cheung G, Ma Y. Face recovery in conference video streaming using robust principal component analysis. In: Proceedings of the 18th IEEE International Conference on Image Processing. Brussels, Belgium: IEEE, 2011. 3225- 3228
[78]
Zhang Z D, Ganesh A, Liang X, Ma Y. TILT: transform invariant low-rank textures. International Journal of Computer Vision, 99(1): 1-24
[79]
Mobahi H, Zhou Z H, Yang A Y, Ma Y. Holistic 3D reconstruction of urban structures from low-rank textures. In: Proceedings of the 2011 International Conference on Computer Vision Workshops. Barcelona: IEEE, 2011. 593-600
[80]
Ren J, Vlachos T. Detection of dirt impairments from archived film sequences: survey and evaluations. SPIE Journal of Optical Engineering, 2010, 49(6): 067005
[81]
Angst R, Zach C, Pollefeys M. The generalized trace-norm and its application to structure-from-motion problems. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 1-8
[82]
Baraniuk R G, Cevher V, Duarte M F, Hegde C. Model-based compressive sensing. IEEE Transactions on Information Theory, 2010, 56(4): 1982-2001
[83]
Bach F. Structured sparsity-inducing norms through submodular functions. In: Proceedings of the 2010 in Advances in Neural Information Processing Systems, 2010. 118-126
[84]
Liu R S, Lin Z C, Wei S M, Su Z X. Solving principal component pursuit in linear time via l_1 filtering [Online], available: http://www.cis.pku.edu.cn/faculty/vision/zlin/zlin.htm, July 5, 2013
[85]
Zhou T Y, Tao D. GoDec: randomized low-rank and sparse matrix decomposition in noisy case. In: Proceedings of the 2011 International Conference on Machine Learning, 2011. 33-40
[86]
Jhuo I, Liu D, Lee D T, Chang S F. Robust visual domain adaptation with low-rank reconstruction. In: Proceedings of the 2012 Computer Vision and Pattern Recognition, 2012. 2168-2175
[87]
Zhou X W, Yang C, Yu W C. Automatic mitral leaflet tracking in echocardiography by outlier detection in the low-rank representation. In: Proceedings of the 2012 Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012. 972-979
