Unifying Nuclear Norm and Bilinear Factorization Approaches for Low-Rank Matrix Decomposition

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Robust reduced-rank filtering for erratic seismic noise attenuation (2015, background): “This approach of low-rank matrix decomposition is referred to as bilinear factorization (Cabral et al., 2013).”
The non-convex geometry of low-rank matrix optimization (2016, background): “[10] worked on a similar problem and showed all global optima of (F1) corresponds to the solution of the convex program (P1).”
Self-Paced Curriculum Learning (2015, methodology): “We considered L 2 -and L 1 -norm MF methods, and incorporated the SPL and SPCL frameworks with the solvers proposed by Cabral et al. (2013) and Wang et al. (2012), respectively .”
Weighted Schatten $p$ -Norm Minimization for Image Denoising and Background Subtraction (2015, methodology): “Speaking, LRMA can be achieved by factorization based models [15], [16], [17], [18] and regularization based models.”
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015, methodology): “al. [458] Zheng et al. [458] LRMF-MOG EM algorithm Oiter=Unknown, Opre=Unknown, Oconv=Unknown Meng et al. [261] Meng et al. [261] UNN-BF ALM Oiter(mnr + nr2), Opre=Unknown, Oconv=Unknown Cabral et al.[42] Cabral et al.[42] RRF Alternative Direction Descent Algorithm (ADDA) Oiter=Unknown, Opre=Unknow”