Missing Data Reconstruction

1) Singular Value Decomposition (1 paper)

I. Kajo, Y. Ruichek, N. Kamel, “History Based Incremental Singular Value Decomposition for Background Initialization and Foreground Segmentation”, Iberoamerican Congress on Pattern Recognition, CIARP 2023, pages 63-75, 2023.

2) Subspace Learning -  Matrix and Tensor Completion (4 papers)

B. Vandereycken, "Low-rank matrix completion by Riemannian optimization", SIAM Journal on Optimization, Volume 23, pages 1214-1236,  2013.

Y. Xu, R. Hao, W. Yin, Z. Su, "Parallel matrix factorization for low-rank tensor completion", Inverse Problem Imaging", Volume 9, pages 601-624, 2015.

I. Sari, A. Juarna, S. Harmanto, D. Kerami, "Background Estimation using Principal Component Analysis based on Limited Memory Block Krylov Subspace Optimization", International Journal of Electrical and Computer Engineering, April 2018.

H. Le, T. Le, J. Wang, Y. Liang,  "Singular Spectrum Analysis for Background Initialization with Spatio-Temporal RGB Color Channel Data", MDPI Entropy, December 2021.

3) Subspace Learning - Robust Matrix and Tensor Completion (14 papers)

Matrix (6)

S. Javed, A. Mahmood, T. Bouwmans, S. Jung, "Motion-Aware Graph Regularized RPCA for Background Modeling of Complex Scenes", Scene Background Modeling Contest, International Conference on Pattern Recognition, ICPR 2016, December 2016.

I. Sari, A. Juarna, S. Harmanto, D. Kerami, “Background Estimation using Principal Component Analysis based on Limited Memory Block Krylov Subspace Optimization”, International Journal of Electrical and Computer Engineering, 2018.

M. Huang, S. Ma, L. Lai, “Robust low-rank matrix completion via an alternating manifold proximal gradient continuation method”, IEEE Transactions on Signal Processing,  Volume 69, pages 2639–2652, 2021.

Y.  He, F. Wang, Y. Li, J. Qin, B. Chen,  "Robust matrix completion via maximum correntropy criterion and half-quadratic optimization", IEEE Transactions on Signal Processing, Volume 68, pages 181-195, 2019.

Z. Li, Z. Hu, F. Nie, R. Wang, X. Li, "Matrix completion with column outliers and sparse noise", Information Sciences, Volume 573, pages 125-140, 2021.

V. Gowda, M. Gopalakrishna, J. Megha, S. Mohankumar, "Background initialization in video data using singular value decomposition and robust principal component analysis", International Journal of Computers and Applications, Volume 45, No. 9, pages 600-609, 2023.

Tensors (6)

S. Javed, T. Bouwmans, S. Jung, “SBMI-LTD: Stationary Background Model Initialization based on Low-rank Tensor Decomposition”, ACM Symposium on Applied Computing, SAC 2017, 2017.

I. Kajo, N. Kamel, Y. Ruichek, A. Mali, “SVD-based Tensor Completion Technique for Background Initialization", IEEE Transaction on Image Processing, 2018.

I. Kajo, N. Kamel,Y. Ruichek, "Self-Motion-Assisted Tensor Completion Method for Background Initialization in Complex Video Sequences”, IEEE Transactions on Image Processing, 2019.

T. Jiang, T. Huang, X. Zhao, L. Deng, “A novel nonconvex approach to recover the low-tubal-rank tensor data: when t-SVD meets PSSV”, Preprint, 2018.

B. Madathil, S. George, “Twist tensor total variation regularized-reweighted nuclear norm based tensor completion for video missing area recovery", Information Sciences, Volume 423, pages 376–397, 2018.

J. Zheng, W. Wang, X. Zhang, X. Jiang,“A Novel Tensor Factorization-Based Method with Robustness to Inaccurate Rank Estimation”, Preprint, 2023.

Surveys (2)

A. Sobral, T. Bouwmans, E. Zahzah, "Comparison of Matrix Completion Algorithms for Background Initialization in Videos”, SBMI 2015 Workshop in conjunction with ICIAP 2015, Genova, Italy, September 2015.

A. Sobral, E. Zahzah, "Matrix and tensor completion algorithms for background model initialization: A comparative evaluation", Pattern Recognition Letters, December 2016.