The different robust subspace learning/tracking frameworks which are based on decomposition into low-rank plus additive tensors are the following ones:
Tensor RPCA (159 papers)
Robust Non negative Tensor Factorization (2 papers)
Robust Tensor Completion (21 papers)
Robust Tensor Recovery (10 papers)
Robust Low Rank Minimization (11 papers)
Robust Tensor Subspace Tracking (6 papers)
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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]
A. Sobral, C. Baker, T. Bouwmans, E. Zahzah, “Incremental and Multi-feature Tensor Subspace Learning applied for Background Modeling and Subtraction”, International Conference on Image Analysis and Recognition, ICIAR 2014, October 2014.
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