Robust Matrix Completion

Matrix completion aims at recovering a low rank matrix from partial observations of its entries. Robust matrix completion RMC, also called RPCA plus matrix completion problem can also be used for computer vision problems.

Batch Matrix Completion (25 papers)

• • Robust Matrix Completion with l1-norm loss function  (RMC-l1) (1 paper)

  • Robust Matrix Completion with lσ-norm loss function (RMC-lσ) (1 paper)

  • Robust Matrix Completion with lp-nom loss function (RMC-lp) (1 paper)

  • Robust Bilateral Factorization (RMC-RBF)  (2 papers)

  • Robust Matrix Completion with Convex Formulation (1 paper)

  • Robust Matrix Completion with Matrix Factorization (RMC-MF) (1 paper)

  • Robust Matrix Completion with Greedy Low-rank Algorithm (RMC-GL) (1 paper)

  • Factorized Robust Matrix Completion (FRMC) (1 paper)

  • Motion-Assisted Matrix Completion (MAMC) (2 papers)

  • Nearly-optimal Robust Matrix Completion (N-RMC) (1 paper)

  • Fast Robust Matrix Completion (FRMC) (3 papers)

  • Correntropy based Robust Matrix Completion (C-RMC) (1 paper)

  • Deterministic Sampling Patterns (DSP-RMC) (1 paper)

  • Mixture Matrix Completion (MMC) (1 paper)

  • Comparison of Matrix Completion Algorithms (2 papers)

J. Li, J. Cai, H. Zhao, “Robust Inexact Alternating Optimization for Matrix Completion With Outliers”, Journal of Computational Mathematics, Volume 38, No.2, pages 337-354, 2020

M. Huang, S. Ma, L. La, “Robust low-rank matrix completion via an alternating manifold proximal gradient continuation method”, Preprint, 2020.

Y. Chen, H. Xu, C. Caramanis, S. Sanghavi, "Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs", IEEE Transactions on Information Theory. Volume 62, No.1, pages 503-526, 2016.

F. Nie, Z. Li, Z. Hu, R. Wang, X. Li, "Robust Matrix Completion with Column Outliers”, IEEE Transactions on Cybernetics, 2021.

H. Cai, L. Huang, C. Kundu, B. Su, On the Robustness of Cross-Concentrated Sampling for Matrix Completion, Preprint, January 2024.

Online Matrix Completion (3 papers)

A. Akhriev, J. Marecek, A. Simonetto, “Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise”, Preprint, September 2018.

P. Narayanamurthy, V. Daneshpajooh, N. Vaswani, "Provable memory-efficient online robust matrix completion", IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, May 2019.

C. Liu, C. Chen, H. Shan, B. Wang, "Robust Online Matrix Completion with Gaussian Mixture Model”,  IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, Barcelona, Spain, 2020.

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, "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]

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