1 - Detecting Contiguous Outlier via Low-Rank Representation (6 papers)
X. Zhou, C. Yang W. Yu, “Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 2012, pages 597-610, 2012.
C. Sudarshan, K Hemanth, S. Yellampalli, “Modelling DECOLOR on low-rank matrix with sparsity decomposition for drawing contour outlier on video”, IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, RTEICT 2017, pages 569-576, Bangalore, India, 2017.
M. Shakeri, H. Zhang, “COROLA: A Sequential Solution to Moving Object Detection using Low-rank Approximation”, Preprint, May 2015.
B. Chen, L. Shi, X. Ke, "Low-Rank Representation with Contextual Regularization for Moving Object Detection in Big Surveillance Video Data", IEEE International Conference on Multimedia Big Data, BigMM 2017, pages 134-141, 2017.
B. Chen, L. Shi, X. Ke, "A Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance", IEEE Transactions on Big Data, 2017
Y. Zhou, B. Ling, "Detecting moving objects via the low-rank representation", Signal, Image and Video Processing, pages 1-9, 2019.
2 - Robust Matrix Factorization (1 paper)
L. Xiong, X. Chen, J. Schneider, “Direct Robust Matrix Factorization for Anomaly Detection”, International Conference on Data Mining, ICDM 2011, 2011
3 - Probabilistic Robust Matrix Factorization (1 paper)
N. Wang, T. Yao, J. Wang, D. Yeung, “A Probabilistic Approach to Robust Matrix Factorization”, European Conference on Computer Vision, ECCV 2012, 2012.
4 - Bayesian Robust Matrix Factorization (3 papers)
N. Wang, D. Yeung, “Bayesian Robust Matrix Factorization for Image and Video Processing”, International Conference on Computer Vision, ICCV 2013, 2013.
Q. Zhao, D. Meng, Z. Xu, W. Zuo, Y. Yan, “l1-Norm Low-Rank Matrix Factorization by Variational Bayesian Method”, IEEE Transactions on Neural Networks and Learning Systems, Volume 26, No. 4, pages 825-839, April 2015.
H. Xie, C. Li, R. Xu, L. Mergensen, “Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation”, International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, pages 484-495, January 2020.
5 - Robust Matrix Factorization with noises (9 papers)
Y. Zheng, G. Liu, S. Sugimoto, S. Yan, M. Okutomi, "Practical low-rank matrix approximation under robust l1-norm", CVPR 2012, 2012.
D. Meng, F. De la Torre, “Robust Matrix Factorization with Unknown Noise”, International Conference on Computer Vision, ICCV 2013, 2013.
X. Wang, S. Gou, X. Wang, Y. Zhao, L. Zhang, "Patch-Based Gaussian Mixture Model for Concealed Object Detection in Millimeter-Wave images", TENCON 2018, pages 2522-2527, 2018.
X. Xu, P. Yang, H. Xian, Y. Liu, "Robust moving objects detection in long-distance imaging through turbulent medium", Infrared Physics and Technology, 2019.
X. Cao,Y. Chen, Q. Zhao, D. Meng, Y. Wang, D. Wang, Z. Xu, "Low-rank Matrix Factorization under General Mixture Noise Distributions" ,IEEE International Conference on Computer Vision, ICCV 2015, 2015.
H. Yong, D. Meng, W. Zuo, L. Zhang, "Robust Online Matrix Factorization for Dynamic Background Subtraction", IEEE Transaction on Pattern Analysis and Machine Intelligence", 2017.
S. Xu, C. Zhang, J. Zhang, “Adaptive Quantile Low-Rank Matrix Factorization”, Preprint, January 2019.
W. Munir, A. Siddiqui, M. Imran, I. Tauqir, N. Zulfiqar, W. Iqbal, A. Ahmad , “Background subtraction in videos using LRMF and CWM algorithm”, Journal of Real-Time Image Processing, 2021.
P. Wang, D. Zhang, Z. Lu, l. Li, "Moving object detection based on reliability low-rank factorization and generalized diversity difference", Journal of Computer Applications, Volume 43, No. 2, pages 514-520, 2023.
6 - Holistic Robust Matrix Factorization (2 papers)
R. Cabral, F. De la Torre, J. Costeira, A. Bernardino, “Unifying Nuclear Norm and Bilinear Factorization Approaches for Low-rank Matrix Decomposition”, International Conference on Computer Vision, ICCV 2013, 2013.
E. Kim, M. Lee, S. Oh,"Elastic-Net Regularization of Singular Values for Robust Subspace Learning", IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, 2015.
7 - Robust Rank Matrix Factorization (1 paper)
H. Sheng, W. Suzhen, W. Xin,"l1-regularized Outlier Isolation and Regression", Preprint, June 2014.
8 - Robust Orthogonal Matrix Factorization (1 paper)
E. Kim, S. Oh, "Robust orthogonal matrix factorization for efficient subspace learning", Neurocomputing, April 2015.
9. Low-Rank Matrix Recovery (7 papers)
X. Zhang, Y. Gao, L. Lan, X. Guo, X. Huang, Z. Luo, “Low-Rank Matrix Recovery via Continuation-Based Approximate Low-Rank Minimization”, Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018, pages 559-573, July 2018.
X. Guo, Z. Lin, "ROUTE: Robust Outlier Estimation for Low Rank Matrix Recovery", International Joint Conference on Artificial Intelligence, IJCAI 2017, pages 1746-1752, Melbourne, Australia, 2017.
X. Guo, Z. Lin, “Low-Rank Matrix Recovery via Robust Outlier Estimation”, IEEE Transactions on Image Processing, Volume 27, No. 11, pages 5316-5327, November 2018.
X. Li, Z. Zhu, A. Man-Cho So, R. Vidal, “Nonconvex robust low-rank matrix recovery", SIAM Journal on Optimization, Volume 30, No. 1, pages 660–686, February 2020.
J. Ma, S. Fattahi, “Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization”, Preprint, February 2022.
Z. Wang, H. So, A. Zoubir, "Robust Low-Rank Matrix Recovery via Hybrid Ordinary-Welsch Function”, IEEE Transactions on Signal Processing, Volume 71, pages 2548-2563, 2023.
X. Liu, Y. Dou, J. Wang, “Modified correlated total variation regularization for low-rank matrix recovery”, Signal, Image and Video Processing, June 2024.
10 - Weighted Low-Rank Matrix Approximation (6 papers)
A. Dutta, X. Li, "On a problem of weighted low-rank approximation of matrices", SIAM Journal on Matrix Analysis and Applications, 2017.
A. Dutta, X. Li, "A Fast Algorithm for a Weighted Low-Rank Approximation", International Conference on Machine Vision Applications, May 2017.
A. Dutta, X Li, “Weighted Low Rank Approximation for Background Estimation Problems”, International Workshop on RSL-CV in conjunction with ICCV 2017, October 2017.
A. Dutta, X Li, P. Richtarik, "A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices", International Workshop on RSL-CV in conjunction with ICCV 2017, October 2017.
A. Dutta, X. Li, P. Richtarik, “Weighted Low-Rank Approximation of Matrices and Background Modeling”, Preprint, 2018.
A. Dutta, P. Richtarik, "Online and Batch Supervised Background Estimation via L1 Regression", IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 2019.
11 - Majorization Minimization (2 papers)
Z. Lin, C. Xu, H. Zha,"Robust Matrix Factorization by Majorization Minimization", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
Q. Yao, J. Kwok, "Scalable Robust Matrix Factorization with Nonconvex Loss", Conference on Neural Information Processing Systems, NIPS 2018, December 2018.
12 - Online Matrix Factorization (3 papers)
X. Guo, "Online Robust Low Rank Matrix Recovery", International Joint Conference on Artificial Intelligence, IJCAI 2015, July 2015.
J. Wang, Y. Zhao, K. Zhang, Q. Wang, X. Li, "Spatio-Temporal Online Matrix Factorization for Multi-scale Moving Objects Detection", IEEE Transactions on Circuits and Systems for Video Technology, April 2021.
Q. Li, X. Li, "Efficient Low-Rank Matrix Factorization based on ℓ1,ε-norm for Online Background Subtraction", IEEE Transactions on Circuits and Systems for Video Technology, 2021.
13 - Non-convex Low Rank Representation (2 papers)
Y. Chen, Y. Wang, M. Li, G. He, "Augmented Lagrangian Alternating Direction Method for Low-Rank Minimization via Non-Convex Approximation", Signal, Image and Video Processing, SIViP 2017, 2017.
J. Zhao, "A Novel Low-Rank Matrix Approximation Algorithm for Face Denoising and Background/Foreground Separation", Computational and Applied Mathematics, 2022.
14 - Multi-scale Low Rank Approximation (1 paper)
M. Abdolali, M. Rahmati,"Multi Scale Decomposition in Low Rank Approximation", IEEE Signal Processing Letters, 2017.
15 - UTV Decompositions (1 paper)
M. Kaloorazi, R. de Lamare, "Compressed Randomized UTV Decompositions for Low-Rank Matrix Approximations", IEEE Journal of Selected Topics in Signal Processing, December 2018.
16 - Rank-1 Approximation (1 paper)
F. Bossmann, J. Ma, “Enhanced image approximation using shifted rank-1 reconstruction”, preprint, October 2018.
17 - Inliers Selection (1 paper)
Z. Hu, F. Nie, X. Li, "Robust Low Rank Approximation via Inliers Selection”, IEEE International Conference on Image Processing, ICIP 2018, pages 3688-3692, Athens, Greece, 2018.
18 - Iterative Reconstrained Representation (1 paper)
J. Zheng, C. Lu, H. Yu, W. Wang, “Iterative Reconstrained Low-Rank Representation via Weighted Nonconvex Regularizer”, IEEE Access, Volume 6, pages 51693- 51707, October 2018.
19 - Robust Structured Low-Rank Approximation (2 papers)
C. Hage, M. Kleinsteuber, "Robust Structured Low-Rank Approximation on the Grassmannian", International Conference on Latent Variable Analysis and Signal Separation, LVA 2015, pages 295-303, 2015.
C. Hage, "Robust Structured and Unstructured Low-Rank Approximation on the Grassmannian", PhD Thesis, TUM, Germany, 2016.
20 - Provable Algorithms (1 paper)
Y. Li, "Provable Algorithms for Scalable and Robust Low-Rank Matrix Recovery", PHD Thesis, Ohio State University, USA, 2018.
21 - Polynomial-time Approximation Scheme (1 paper)
F. Ban., V. Bhattiprolu, K. Bringmann, P. Kolev, E. Lee, D. Woodruk, "A PTAS for lp-Low Rank Approximation", SIAM, pages 747-766, 2019.
22 - Compressive Sensing (3 papers)
X. Shu, N. Ahuja, “Imaging via Three-dimensional Compressive Sampling (3DCS)”, International Conference on Computer Vision, ICCV 2011, 2011.
B. Kang, W. Zhu, J. Yan, “Object detection oriented video reconstruction using compressed sensing”, EURASIP Journal on Advances in Signal Processing Sample, February 2015.
B. Kang, W. Zhu, “Robust moving object detection using compressed sensing”, IET Image Processing, 2015.
23 - Dictionary Low-Rank Representation (1 paper)
J. Zhou, X. Shen, S. Liu, L. Wang, Q. Zhu, P. Qia, "Multi-dictionary induced low-rank representation with multi-manifold regularization", Applied Intelligence, Volume 53, Pages 3576-3593, 2023.
24 - Accelerated Solvers (1 paper)
H. Zhang, B. Wen, Z. Zha, B. Zhang, Y. Tang, G. Yu, W. Du, "Accelerated PALM for Nonconvex Low-rank Matrix Recovery with Theoretical Analysis”, IEEE Transactions on Circuits and Systems for Video Technology, 2023.
25 - Learning based Recovery (1 paper)
Z. Xu, Y. Zhang, C. Ma, Y. Yan, Z. Peng, S. Xie, “LERE: Learning-based Low-Rank Matrix Recovery with Rank Estimation”, AAAI Conference on Artificial Intelligence, pages 16228-16236, 2024.
26 - Triple Component Matrix Factorization (1 paper)
N. Shi, S. Fattahi, R. Kontar, “Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components”, Preprint, March 2024.