Robust principal component analysis?
暂无分享,去创建一个
Yi Ma | E. Candès | Xiaodong Li | John Wright
[1] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[2] C. Eckart,et al. The approximation of one matrix by another of lower rank , 1936 .
[3] Bell Telephone,et al. ROBUST ESTIMATES, RESIDUALS, AND OUTLIER DETECTION WITH MULTIRESPONSE DATA , 1972 .
[4] P. Lions,et al. Splitting Algorithms for the Sum of Two Nonlinear Operators , 1979 .
[5] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[6] F. Ruymgaart. A robust principal component analysis , 1981 .
[7] Peter J. Huber,et al. Robust Statistics , 2005, Wiley Series in Probability and Statistics.
[8] Y. Nesterov. A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .
[9] Jack Yurkiewicz,et al. Constrained optimization and Lagrange multiplier methods, by D. P. Bertsekas, Academic Press, New York, 1982, 395 pp. Price: $65.00 , 1985, Networks.
[10] Richard A. Harshman,et al. Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..
[11] T. Landauer,et al. Indexing by Latent Semantic Analysis , 1990 .
[12] Santosh S. Vempala,et al. Latent semantic indexing: a probabilistic analysis , 1998, PODS '98.
[13] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[14] Robert R. Meyer,et al. A variable-penalty alternating directions method for convex optimization , 1998, Math. Program..
[15] W. Eric L. Grimson,et al. Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[16] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[17] M. Ledoux. The concentration of measure phenomenon , 2001 .
[18] David J. Kriegman,et al. From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[19] Ronen Basri,et al. Lambertian reflectance and linear subspaces , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[20] Stephen P. Boyd,et al. Log-det heuristic for matrix rank minimization with applications to Hankel and Euclidean distance matrices , 2003, Proceedings of the 2003 American Control Conference, 2003..
[21] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[22] Qi Tian,et al. Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.
[23] Michael J. Black,et al. A Framework for Robust Subspace Learning , 2003, International Journal of Computer Vision.
[24] B. Ripley,et al. Robust Statistics , 2018, Wiley Series in Probability and Statistics.
[25] Yurii Nesterov,et al. Smooth minimization of non-smooth functions , 2005, Math. Program..
[26] Takeo Kanade,et al. Robust L/sub 1/ norm factorization in the presence of outliers and missing data by alternative convex programming , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[27] Wotao Yin,et al. An Iterative Regularization Method for Total Variation-Based Image Restoration , 2005, Multiscale Model. Simul..
[28] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[29] Dan Suciu,et al. Journal of the ACM , 2006 .
[30] Lillian Lee Scribes,et al. Latent Semantic Indexing , 2007 .
[31] Y. Nesterov. Gradient methods for minimizing composite objective function , 2007 .
[32] James Bennett,et al. The Netflix Prize , 2007 .
[33] Yin Zhang,et al. Fixed-Point Continuation for l1-Minimization: Methodology and Convergence , 2008, SIAM J. Optim..
[34] Wotao Yin,et al. Bregman Iterative Algorithms for \ell1-Minimization with Applications to Compressed Sensing , 2008, SIAM J. Imaging Sci..
[35] Volkan Cevher,et al. Compressive Sensing for Background Subtraction , 2008, ECCV.
[36] D. Goldfarb,et al. Fixed point and Bregman iterative methods for matrix rank , 2009 .
[37] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2009, Found. Comput. Math..
[38] Hossein Mobahi,et al. Face recognition with contiguous occlusion using markov random fields , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[39] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[40] Lieven Vandenberghe,et al. Interior-Point Method for Nuclear Norm Approximation with Application to System Identification , 2009, SIAM J. Matrix Anal. Appl..
[41] S. Yun,et al. An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems , 2009 .
[42] S. Yun,et al. An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems , 2009 .
[43] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[44] Arvind Ganesh,et al. Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix , 2009 .
[45] Tony Hey,et al. The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .
[46] Emmanuel J. Candès,et al. A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..
[47] Donald Goldfarband Shiqian. CONVERGENCE OF FIXED POINT CONTINUATION ALGORITHMS FOR MATRIX RANK MINIMIZATION , 2010 .
[48] Pablo A. Parrilo,et al. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..
[49] Andrea Montanari,et al. Matrix completion from a few entries , 2009, 2009 IEEE International Symposium on Information Theory.
[50] Stephen Becker,et al. Quantum state tomography via compressed sensing. , 2009, Physical review letters.
[51] John Wright,et al. RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[52] Yi Ma,et al. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.
[53] Emmanuel J. Candès,et al. The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.
[54] Emmanuel J. Candès,et al. Matrix Completion With Noise , 2009, Proceedings of the IEEE.
[55] Shiqian Ma,et al. Convergence of Fixed-Point Continuation Algorithms for Matrix Rank Minimization , 2009, Found. Comput. Math..
[56] Shiqian Ma,et al. Fixed point and Bregman iterative methods for matrix rank minimization , 2009, Math. Program..
[57] David Gross,et al. Recovering Low-Rank Matrices From Few Coefficients in Any Basis , 2009, IEEE Transactions on Information Theory.
[58] Anthony J. G. Hey,et al. The Fourth Paradigm: Data-Intensive Scientific Discovery [Point of View] , 2011 .
[59] Emmanuel J. Candès,et al. NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..
[60] Pablo A. Parrilo,et al. Rank-Sparsity Incoherence for Matrix Decomposition , 2009, SIAM J. Optim..
[61] Benjamin Recht,et al. A Simpler Approach to Matrix Completion , 2009, J. Mach. Learn. Res..
[62] Roman Vershynin,et al. Introduction to the non-asymptotic analysis of random matrices , 2010, Compressed Sensing.
[63] G. Sapiro,et al. A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.
[64] Xiaoming Yuan,et al. Sparse and low-rank matrix decomposition via alternating direction method , 2013 .