Alternating Direction Method of Multipliers for a Class of Nonconvex and Nonsmooth Problems with Applications to Background/Foreground Extraction
暂无分享,去创建一个
[1] Bastian Goldlücke,et al. Variational Analysis , 2014, Computer Vision, A Reference Guide.
[2] Thierry Bouwmans,et al. Traditional and recent approaches in background modeling for foreground detection: An overview , 2014, Comput. Sci. Rev..
[3] John Wright,et al. RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images , 2012, IEEE Trans. Pattern Anal. Mach. Intell..
[4] R. Glowinski,et al. Sur l'approximation, par éléments finis d'ordre un, et la résolution, par pénalisation-dualité d'une classe de problèmes de Dirichlet non linéaires , 1975 .
[5] Zongben Xu,et al. Convergence of multi-block Bregman ADMM for nonconvex composite problems , 2015, Science China Information Sciences.
[6] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[7] Lei Zhang,et al. Discriminative learning of iteration-wise priors for blind deconvolution , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Kim-Chuan Toh,et al. A Convergent 3-Block SemiProximal Alternating Direction Method of Multipliers for Conic Programming with 4-Type Constraints , 2014, SIAM J. Optim..
[9] Hédy Attouch,et al. Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Lojasiewicz Inequality , 2008, Math. Oper. Res..
[10] Constantine Caramanis,et al. Robust PCA via Outlier Pursuit , 2010, IEEE Transactions on Information Theory.
[11] T. Wu,et al. A Class of Linearized Proximal Alternating Direction Methods , 2011, J. Optim. Theory Appl..
[12] J. Horowitz,et al. Asymptotic properties of bridge estimators in sparse high-dimensional regression models , 2008, 0804.0693.
[13] Mila Nikolova,et al. Energy Minimization Methods , 2015, Handbook of Mathematical Methods in Imaging.
[14] Jianqing Fan,et al. COMMENTS ON « WAVELETS IN STATISTICS : A REVIEW , 2009 .
[15] Lei Zhang,et al. Weighted Nuclear Norm Minimization with Application to Image Denoising , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[16] Dimitri P. Bertsekas,et al. On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators , 1992, Math. Program..
[17] Jorge J. Moré,et al. Digital Object Identifier (DOI) 10.1007/s101070100263 , 2001 .
[18] Daniel K Sodickson,et al. Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components , 2015, Magnetic resonance in medicine.
[19] Michael K. Ng,et al. Median filtering‐based methods for static background extraction from surveillance video , 2015, Numer. Linear Algebra Appl..
[20] Guoyin Li,et al. Douglas–Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems , 2014, Math. Program..
[21] Qionghai Dai,et al. Low-Rank Structure Learning via Nonconvex Heuristic Recovery , 2010, IEEE Transactions on Neural Networks and Learning Systems.
[22] Thierry Bouwmans,et al. Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey , 2011 .
[23] Jianhong Shen,et al. Deblurring images: Matrices, spectra, and filtering , 2007, Math. Comput..
[24] Donald Geman,et al. Constrained Restoration and the Recovery of Discontinuities , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[25] Bingsheng He,et al. Linearized Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming , 2011 .
[26] Yong Yu,et al. Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Jianqing Fan,et al. Comments on «Wavelets in statistics: A review» by A. Antoniadis , 1997 .
[28] 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.
[29] Hong-Kun Xu,et al. Convergence of Bregman alternating direction method with multipliers for nonconvex composite problems , 2014, 1410.8625.
[30] Zhixun Su,et al. Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning , 2013, Machine Learning.
[31] Su-In Lee,et al. Node-based learning of multiple Gaussian graphical models , 2013, J. Mach. Learn. Res..
[32] Zhi-Quan Luo,et al. Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems , 2014, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[33] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[34] Soon Ki Jung,et al. Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset , 2015, Comput. Sci. Rev..
[35] Stephen P. Boyd,et al. Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.
[36] Mingyi Hong,et al. Alternating direction method of multipliers for penalized zero-variance discriminant analysis , 2014, Computational Optimization and Applications.
[37] Mila Nikolova,et al. Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization , 2008, SIAM J. Imaging Sci..
[38] Qinghua Hu,et al. Efficient Background Modeling Based on Sparse Representation and Outlier Iterative Removal , 2016, IEEE Transactions on Circuits and Systems for Video Technology.
[39] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[40] Qi Tian,et al. Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.
[41] Cun-Hui Zhang. Nearly unbiased variable selection under minimax concave penalty , 2010, 1002.4734.
[42] Wotao Yin,et al. Global Convergence of ADMM in Nonconvex Nonsmooth Optimization , 2015, Journal of Scientific Computing.
[43] Thierry Bouwmans,et al. Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance , 2014, Comput. Vis. Image Underst..
[44] Kim-Chuan Toh,et al. A Convergent 3-Block Semi-Proximal ADMM for Convex Minimization Problems with One Strongly Convex Block , 2014, Asia Pac. J. Oper. Res..
[45] Xiaojun Chen,et al. Linearly Constrained Non-Lipschitz Optimization for Image Restoration , 2015, SIAM J. Imaging Sci..
[46] Xiaojun Chen,et al. Smoothing Nonlinear Conjugate Gradient Method for Image Restoration Using Nonsmooth Nonconvex Minimization , 2010, SIAM J. Imaging Sci..
[47] Paul Tseng,et al. Hankel Matrix Rank Minimization with Applications to System Identification and Realization , 2013, SIAM J. Matrix Anal. Appl..
[48] Wenjiang J. Fu,et al. Asymptotics for lasso-type estimators , 2000 .
[49] Bingsheng He,et al. The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent , 2014, Mathematical Programming.
[50] Guoyin Li,et al. Global Convergence of Splitting Methods for Nonconvex Composite Optimization , 2014, SIAM J. Optim..
[51] Dianne P. O'Leary,et al. Deblurring Images: Matrices, Spectra and Filtering , 2006, J. Electronic Imaging.
[52] John N. Tsitsiklis,et al. Parallel and distributed computation , 1989 .
[53] B. Mercier,et al. A dual algorithm for the solution of nonlinear variational problems via finite element approximation , 1976 .
[54] Marc Teboulle,et al. Proximal alternating linearized minimization for nonconvex and nonsmooth problems , 2013, Mathematical Programming.