Robust Principal Component Analysis
RPCA is based on the decomposition in low-rank and sparse matrices. The different methods differ from the decomposition, the corresponding optimization problem and the solvers. These different approaches can be classified in the following categories.
Principal Component Pursuit (PCP) (490 papers)
Outlier Pursuit (OP) (3 papers)
Sparsity Control (SpaCtrl) (3 papers)
Sparse Corruptions (SpaCorr) (1 paper)
Log-sum Heuristic Recovery (LHR) (3 papers)
Iteratively Reweighted Least Squares (IRLS) (5 papers)
Stochastic Approximation (SA) (14 papers)
Bayesian RPCA (BRPCA) (12 papers)
Approximated RPCA (GoDec) (7 papers)
Dictionary sparse based RPCA (D-RPCA) (4 papers)
Sparse Additive Matrix Factorization (SAMF) (2 papers)
Variational Bayesian Sparse Estimator (VBSE) (2 papers)
Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ. Rochelle, France.
Fair Use Policy
As this website gives many information that come from my research, please cite my following survey papers:
T. Bouwmans . A. Sobral, S. Javed, S. Jung, E. Zahzah, "Background/Foreground Separation via Decomposition in Low-rank and Additive Matrices: A Review for a Comparative Evaluation with a Large-Scale Dataset", to be submitted.
T. Bouwmans, E. Zahzah, “Robust PCA via Principal Component Pursuit: A Review for a Comparative Evaluation in Video Surveillance”, Special Issue on Background Models Challenge, Computer Vision and Image Understanding, CVIU 2014, Volume 122, pages 22–34, May 2014. [pdf]
Note: My publications are available on Academia, Research Gate, Researchr, Science Stage and Publication List.