Background Modeling via RPCA

RPCA-PCP

Principal Component Pursuit (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.

LRSD (J. Yang, Department of Mathematics, Nanjing University, China)

X. Yuan, J. Yang, Sparse and Low-Rank Matrix Decomposition Via Alternating Direction Methods, Pacific Journal of Optimization, Volume 9, Issue 1, pages 167-180, 2013.

LMaFit (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.

Fast PCP (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.

incPCP (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.

Recursive Projected Compressive Sensing (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.

Practical ReProCS (Han 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.

Reinforced RPCA (P. Brahma, University of Florida, USA)

P. Brahma, Y. She, S. Li, D. Wu, "Reinforced Robust Principal Component Pursuit", IEEE Transactions on Neural Networks and Learning Systems, 2016.

l0-l1 Regularization (Jing Liu, UCSD, USA)

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

rSVDdpd (S. Roy, Indian Statistical Institute, Kolkota, India)

S. Roy, A. Basu, A. Ghosh, "A new robust scalable singular value decomposition algorithm for video surveillance background modelling", Preprint, 2021.

RPCA-SPCP

R2PCP (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.

NSA (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.

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

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

BiG-AMP (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.

Divide and Conquer Matrix Factorization (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

TFOCS (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

Outlier Pursuit (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

Bayesian RPCA (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.

Variational Bayesian RPCA (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.

Factorized Variational Bayesian RPCA (C. Aicher, Santa Fe Institute, USA)

C. Aicher, “A Variational Bayes Approach to Robust Principal Component Analysis”, REU 2013, 2013.

GoDec (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.

SemiSoftGoDec (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.