Fast L1-Minimization Algorithms For Robust Face Recognition
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Allen Y. Yang | S. Shankar Sastry | Zihan Zhou | Yi Ma | A. G. Balasubramanian | S. Sastry | A. Yang | Arvind Ganesh | Zihan Zhou | Yi Ma | A. Balasubramanian | A. Balasubramanian
[1] Michael Elad,et al. From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..
[2] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[3] Yaakov Tsaig,et al. Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.
[4] Michael P. Friedlander,et al. Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..
[5] Wotao Yin,et al. Bregman Iterative Algorithms for \ell1-Minimization with Applications to Compressed Sensing , 2008, SIAM J. Imaging Sci..
[6] Stephen J. Wright,et al. Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.
[7] Dmitry M. Malioutov,et al. Homotopy continuation for sparse signal representation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..
[8] I. Loris. On the performance of algorithms for the minimization of ℓ1-penalized functionals , 2007, 0710.4082.
[9] John Wright,et al. Dense Error Correction Via $\ell^1$-Minimization , 2010, IEEE Transactions on Information Theory.
[10] N. Megiddo. Pathways to the optimal set in linear programming , 1989 .
[11] Stephen J. Wright,et al. Computational Methods for Sparse Solution of Linear Inverse Problems , 2010, Proceedings of the IEEE.
[12] MaYi,et al. Dense error correction via l1-minimization , 2010 .
[13] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[14] Narendra Karmarkar,et al. A new polynomial-time algorithm for linear programming , 1984, Comb..
[15] Arian Maleki,et al. Optimally Tuned Iterative Reconstruction Algorithms for Compressed Sensing , 2009, IEEE Journal of Selected Topics in Signal Processing.
[16] Dimitri P. Bertsekas,et al. Constrained Optimization and Lagrange Multiplier Methods , 1982 .
[17] J. Borwein,et al. Two-Point Step Size Gradient Methods , 1988 .
[18] D. Donoho. For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .
[19] Michael Elad,et al. L1-L2 Optimization in Signal and Image Processing , 2010, IEEE Signal Processing Magazine.
[20] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[21] Lei Zhang,et al. Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.
[22] Renato D. C. Monteiro,et al. Interior path following primal-dual algorithms. part I: Linear programming , 1989, Math. Program..
[23] Marc Teboulle,et al. Interior Gradient and Proximal Methods for Convex and Conic Optimization , 2006, SIAM J. Optim..
[24] Brendan J. Frey,et al. Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.
[25] Richard G. Baraniuk,et al. Bayesian Compressive Sensing Via Belief Propagation , 2008, IEEE Transactions on Signal Processing.
[26] M. R. Osborne,et al. A new approach to variable selection in least squares problems , 2000 .
[27] David J. Kriegman,et al. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.
[28] Patrick L. Combettes,et al. Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..
[29] Mark D. Plumbley. Recovery of Sparse Representations by Polytope Faces Pursuit , 2006, ICA.
[30] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.
[31] E. Candès,et al. Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.
[32] E.J. Candes. Compressive Sampling , 2022 .
[33] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[34] Tom Goldstein,et al. The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..
[35] R. Tibshirani,et al. Least angle regression , 2004, math/0406456.
[36] I. Daubechies,et al. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.
[37] José M. Bioucas-Dias,et al. Fast Image Recovery Using Variable Splitting and Constrained Optimization , 2009, IEEE Transactions on Image Processing.
[38] Takeo Kanade,et al. Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[39] J. Hiriart-Urruty,et al. Convex analysis and minimization algorithms , 1993 .
[40] Andrea Montanari,et al. Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.
[41] William W. Hager,et al. Gradient-Based Methods for Sparse Recovery , 2009, SIAM J. Imaging Sci..
[42] Mário A. T. Figueiredo,et al. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.
[43] Olgica Milenkovic,et al. Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.
[44] Ronen Basri,et al. Lambertian reflectance and linear subspaces , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[45] David L. Donoho,et al. De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.
[46] Thomas S. Huang,et al. Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[47] José M. Bioucas-Dias,et al. An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems , 2009, IEEE Transactions on Image Processing.
[48] C. Roos,et al. On the classical logarithmic barrier function method for a class of smooth convex programming problems , 1992 .
[49] K. Schittkowski,et al. NONLINEAR PROGRAMMING , 2022 .
[50] Hedvig Kjellström,et al. IEEE International Conference on Automatic Face and Gesture Recognition , 2013 .
[51] Allen Y. Yang,et al. Distributed Sensor Perception via Sparse Representation , 2010, Proceedings of the IEEE.
[52] Zihan Zhou,et al. Sparsity and Robustness in Face Recognition A tutorial on how to apply the models and tools correctly , 2011 .
[53] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[54] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[55] Luca Zanni,et al. Gradient projection methods for quadratic programs and applications in training support vector machines , 2005, Optim. Methods Softw..
[56] Wotao Yin,et al. TR 0707 A Fixed-Point Continuation Method for ` 1-Regularized Minimization with Applications to Compressed Sensing , 2007 .
[57] Hugo Van hamme,et al. Compressive Sensing for Missing Data Imputation in Noise Robust Speech Recognition , 2010, IEEE Journal of Selected Topics in Signal Processing.
[58] S. Mallat,et al. Adaptive greedy approximations , 1997 .
[59] Y. Nesterov. A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .
[60] Enrico Magli,et al. Distributed Compressed Sensing , 2015 .
[61] D K Smith,et al. Numerical Optimization , 2001, J. Oper. Res. Soc..
[62] Allen Y. Yang,et al. Fast ℓ1-minimization algorithms and an application in robust face recognition: A review , 2010, 2010 IEEE International Conference on Image Processing.
[63] Junfeng Yang,et al. Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..
[64] Allen Y. Yang,et al. Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[65] C. Kelley. Iterative Methods for Linear and Nonlinear Equations , 1987 .
[66] M. Salman Asif. Primal dual pursuit: a homotopy based algorithm for the Dantzig selector , 2008 .
[67] Shinji Mizuno,et al. Theoretical convergence of large-step primal—dual interior point algorithms for linear programming , 1993, Math. Program..
[68] Emmanuel J. Candès,et al. Templates for convex cone problems with applications to sparse signal recovery , 2010, Math. Program. Comput..
[69] X. Jin. Factor graphs and the Sum-Product Algorithm , 2002 .
[70] Michael Elad,et al. Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization , 2007 .
[71] Stephen P. Boyd,et al. An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.
[72] Emmanuel J. Candès,et al. NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..
[73] Guillermo Sapiro,et al. Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.
[74] Y. Nesterov. Gradient methods for minimizing composite objective function , 2007 .