The different problem formulations for robust subspace learning/tracking frameworks which are based on decomposition into low-rank plus additive matrices are the following ones:
Robust Principal Component Analysis (RPCA) (561 papers)
Robust Non-negative Matrix Factorization (RNMF) (6 papers)
Robust Matrix Completion (RMC) (29 papers)
Robust Subspace Recovery (RSR) (9 papers)
Robust Subspace Tracking (RST) (27 papers) [Slow Changes]
Robust Subspace Change-Point Detection (RSCD) (2 papers) [Abrupt Changes]
Robust Low Rank Minimization (RLRM) (62 papers)
Robust Graph Learning (RGL) (1 paper)
Robust Graphical Lasso (1 paper)
Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ. Rochelle, France.
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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, "Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset", Computer Science Review, Volume 23, pages 1-71, February 2017. [pdf]
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]
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