Rank-Sparsity Incoherence for Matrix Decomposition
暂无分享,去创建一个
Pablo A. Parrilo | Venkat Chandrasekaran | Sujay Sanghavi | Alan S. Willsky | A. Willsky | P. Parrilo | S. Sanghavi | V. Chandrasekaran
[1] J. Schur. Bemerkungen zur Theorie der beschränkten Bilinearformen mit unendlich vielen Veränderlichen. , 1911 .
[2] J. Goodman. Introduction to Fourier optics , 1969 .
[3] Jozef Gruska. Mathematical Foundations of Computer Science 1977 , 1977, Lecture Notes in Computer Science.
[4] Leslie G. Valiant,et al. Graph-Theoretic Arguments in Low-Level Complexity , 1977, MFCS.
[5] 丸山 徹. Convex Analysisの二,三の進展について , 1977 .
[6] Charles R. Johnson,et al. Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.
[7] Eduardo D. Sontag,et al. Mathematical Control Theory: Deterministic Finite Dimensional Systems , 1990 .
[8] G. Watson. Characterization of the subdifferential of some matrix norms , 1992 .
[9] Thomas Kailath,et al. Phase-shifting masks for microlithography: automated design and mask requirements , 1994 .
[10] Joe W. Harris,et al. Algebraic Geometry: A First Course , 1995 .
[11] Satyanarayana V. Lokam. Spectral methods for matrix rigidity with applications to size-depth tradeoffs and communication complexity , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.
[12] G. Papavassilopoulos,et al. On the rank minimization problem over a positive semidefinite linear matrix inequality , 1997, IEEE Trans. Autom. Control..
[13] Michael I. Jordan. Graphical Models , 1998 .
[14] B. Codenotti. Matrix Rigidity , 1999 .
[15] Xiaoming Huo,et al. Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.
[16] Satyanarayana V. Lokam. Spectral Methods for Matrix Rigidity with Applications to Size-Depth Trade-offs and Communication Complexity , 2001, J. Comput. Syst. Sci..
[17] Kim-Chuan Toh,et al. SDPT3 — a Matlab software package for semidefinite-quadratic-linear programming, version 3.0 , 2001 .
[18] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[19] 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..
[20] J. Lofberg,et al. YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).
[21] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[22] Alan M. Frieze,et al. Random graphs , 2006, SODA '06.
[23] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[24] A. Banerjee. Convex Analysis and Optimization , 2006 .
[25] Meena Mahajan,et al. On the Complexity of Matrix Rank and Rigidity , 2007, Theory of Computing Systems.
[26] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2009, Found. Comput. Math..
[27] A. Willsky,et al. Sparse and low-rank matrix decompositions , 2009, 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[28] John Wright,et al. Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization , 2009, NIPS.
[29] Pablo A. Parrilo,et al. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..
[30] Pablo A. Parrilo,et al. Latent variable graphical model selection via convex optimization , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[31] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[32] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..
[33] Shiqian Ma,et al. Alternating Direction Methods for Latent Variable Gaussian Graphical Model Selection , 2012, Neural Computation.