Robust Component Analysis

RPCA-PCP

Code Matlab (J. Wright, Perception and Decision Lab, University of Illinois, USA)

E. Candes, X. Li, Y. Ma, J. Wright, “Robust Principal Component Analysis?”, ACM, Volume 58, No. 3, May 2011.

J. Wright, Y. Peng, Y. Ma, A. Ganesh, S. Rao, “Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices by Convex Optimization”, Neural Information Processing Systems, NIPS 2009, December 2009.

Code Matlab (Y. Zhang, Department of Computational and Applied Mathematics, Rice University, U.S.A)

Y. Shen, Z. Wen, Y. Zhang, "Augmented Lagrangian Alternating Direction Method for Matrix Separation based on Low-Rank Factorization", Rice CAAM Tech Report TR11-02, 2011.

Code Matlab (B. Wohlberg, Los Alamos University, USA)

P. Rodríguez, B. Wohlberg, "Fast Principal Component Pursuit Via Alternating Minimization", IEEE International Conference on Image Processing, ICIP 2013, Melbourne, Autralia, September, 2013.

Code Matlab (B. Wohlberg, Los Alamos University, USA)

P. Rodríguez, B. Wohlberg, "A Matlab Implementation of a Fast Incremental Principal Component Pursuit Algorithm for Video Background Modeling", IEEE International Conference on Image Processing, ICIP 2014, October 2014.

P. Rodríguez, B. Wohlberg, "Incremental Principal Component Pursuit for Video Background Modeling", IEEE Signal Processing Letters, 2014.

Code Matlab (C. Qiu, Department of Electrical and Computer Engineering, Iowa State University, USA)

C. Qiu, N. Vaswani, “Real-time Robust Principal Components Pursuit”, International Conference on Communication Control and Computing, 2010.

C. Qiu, N. Vaswani, “Support Predicted Modified-CS for Recursive Robust Principal Components' Pursuit”, IEEE International Symposium on Information Theory, ISIT 2011, 2011.

C. Qiu, N. Vaswani, “ReProCS: A Missing Link between Recursive Robust PCA and Recursive Sparse Recovery in Large but Correlated Noise”, Preprint, 2011.

Code Matlab (H. Guo, Department of Electrical and Computer Engineering, Iowa State University, USA)

H. Guo, C. Qiu, N. Vaswani, "Practical ReProCS for Separating Sparse and Low-dimensional Signal Sequences their Sum - Part 1", International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014, 2014.

H. Guo, C. Qiu, N. Vaswani, "Practical ReProCS for Separating Sparse and Low-dimensional Signal Sequences their Sum - Part 2", GlobalSIP 2014, 2014.

H. Guo, C. Qiu, N. Vaswani, "An Online Algorithm for Separating Sparse and Low-dimensional Signal Sequences from their Sum”, IEEE Transactions on Signal Processing, 2014.

Code Matlab (Y. Chen, Cornell University, USA)

X. Yi, D. Park., Y. Chen, C. Caramanis, "Fast Algorithms for Robust PCA via Gradient Descent", Preprint, May 2016.

Code Matlab (Xin Liu, Oulu University, Finland)

J. Yao, X. Liu, C. Qi, "Foreground detection using low rank and structured sparsity", IEEE International Conference on Multimedia and Expo, ICME 2014, pages 1-6, 2014.

X. Liu, G. Zhao, J. Yao, C. Qi, "Background Subtraction based on low-rank model and structured sparse decomposition", IEEE Transactions on Image Processing, 2015.

Code Matlab (Z. Xue, Nanjing Tech University, China)

Z. Xue, J. Dong, Y. Zhao, C. Liu, R. Chellali, “Low-rank and sparse matrix decomposition via the truncated nuclear norm and a sparse regularizer”, The Visual Computer, May 2018.

Code Matlab (J. Liu, UCSD, USA)

J. Liu, B. Rao, "Robust PCA via l0-l1 Regularization”, IEEE Transactions on Signal Processing, December 2018.

RPC-SPCP

Code Matlab (T. Wu, Institute for Mathematics and Scientific Computing, University of Graz, Austria)

M. Hintermüller, T. Wu, “Robust Principal Component Pursuit via Inexact Alternating Minimization on Matrix Manifolds”, Journal of Mathematics and Imaging Vision, 2014.

Code Matlab (N. Aybat, Industrial Engineering Department, Penn State University, USA)

N. Aybat, D. Goldfarb, G. Iyengar, “Fast First-Order Methods for Stable Principal Component Pursuit”, Preprint 2011.

N. Aybat, D. Goldfarb, G. Iyengar, “Efficient Algorithms for Robust and Stable Principal Component Pursuit”, Preprint 2012.

Code Matlab (P. Schniter, Department of ECE, The Ohio State University, USA)

J. Parker, P. Schniter, V. Cevher, “Bilinear Generalized Approximate Message Passing”, October 2013

J. Parker, P. Schniter, “Bilinear Generalized Approximate Message Passing (BiG-AMP) for Matrix Completion”, Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2012.

Code Matlab (L. Mackey, EECS department, Berkeley University, USA)

L. Mackey, A. Talwalkar, M. Jordan, “Divide-and-Conquer Matrix Factorization”, Neural Information Processing Systems, Neural Information Processing Systems, NIPS 2011, Granada, Spain, December 2011.

RPCA-QPCP

Code Matlab (S. Becker, California Institute of Technology, USA)

S. Becker, E. Candes, M. Grant, “TFOCS: Flexible First-order Methods for Rank Minimization”, Low-rank Matrix Optimization Symposium, SIAM Conference on Optimization, 2011.

RPCA-OP

Code Matlab (H. Xu, National University of Singapore, Singapore)

H. Xu, C. Caramanis, S. Sanghavi, “Robust PCA via Outlier Pursuit”, International Conference on Neural Information Processing System, NIPS 2010, 2010.

Bayesian RPCA

Code Matlab (L. Carin, Department of Electrical and Computer Engineering, Duke University, USA)

X. Ding, L. He, L. Carin, “Bayesian Robust Principal Component Analysis”, IEEE Transaction on Image Processing, 2011.

Code Matlab (S. Derin Babacan, Beckman Institute for Advanced Science and Technology, University of Illinois, USA)

S. Derin Babacan, M. Luessi, R. Molina, and A.K. Katsaggelos, "Sparse Bayesian Methods for Low-Rank Matrix Estimation," IEEE Transactions on Signal Processing, Volume 60, Issue 8, pages 3964-3977, August 2012.

Approximated RPCA

Code Matlab (T. Zhou,Centre for Quantum Computation and Intelligent Systems, University of Technology, Sydney, Australia)

T. Zhou, D. Tao, “GoDec: Randomized Low-rank & Sparse Matrix Decomposition in Noisy Case”, International Conference on Machine Learning, ICML 2011, 2011.