A. Batch algorithms (5 papers)
D. Goldfarb, Z. Qin,"Robust Low-Rank Tensor Recovery: Models and Algorithms", SIAM Journal on Matrix Analysis and Applications, Volume 35, Issue 1, pages 225-253, 2013.
B. Huang, C. Mu, D. Goldfarb, J. Wright, "Provable Low-Rank Tensor Recovery", 2014.
B. Shen, Z. Kong, "Robust Tensor Principal Component Analysis: Exact Recovery via Deterministic Model", Preprint, 2020.
A. Rontogiannis, E. Kofidis, P. Giampouras, “Block-term tensor decomposition: Model selection and computation", IEEE Journal of Selected Topics in Signal Processing, Volume 15, No. 3, pages 464-475, April 2021.
C. Lu, P. Zhou, “Exact recovery of tensor robust principal component analysis under linear transforms", Preprint, 2019.
B. Decomposition (26 papers)
1- CP Decomposition (6 papers)
B. Jiang, S. Ma, S. Zhang, "New Ranks for Even-Order Tensors and Their Applications in Low-Rank Tensor Optimization", Preprint, January 2015.
B. Jiang, S. Ma, S. Zhang, "Low-M-Rank Tensor Completion and Robust Tensor PCA”, IEEE Journal of Selected Topics in Signal Processing, December 2018
J. Shi, S. Zhou, X. Zheng, "Multilinear robust principal component analysis", Acta Electronica Sinica, Volume 42, No. 7, pages 1480-1486, 2014.
T. Yokota, N. Lee, A. Cichocki, "Robust Multilinear Tensor Rank Estimation Using Higher Order Singular Value Decomposition and Information Criteria", IEEE Transactions on Signal Processing, Volume 65, No. 5, pages 1196-1206, March 2017.
R. Karim, “Tensor decompositions and rank approximation of tensors with applications”, PhD Thesis, University of Alabama, 2019.
Z. Wang, G. Sun, “RPCA-related Tensor Decomposition in Foreground/background Modelling”, International Conference on Computing and Pattern Recognition, ICCPR 2023, pages 427-431, 2023.
2- Tucker Decomposition (9 papers)
R. Tomioka, K. Hayashia, H. Sashimi, "Estimation of low-rank tensors via convex optimization", Neural Information Processing Systems, NIPS 2010, 2010.
W. Cao, Y. Wang, J. Sun, D. Meng, C. Yang, A. Cichocki, Z. Xu, “A Novel Tensor Robust PCA Approach for Background Subtraction from Compressive Measurements”, Preprint, March 2015.
W. Cao, Y. Wang, J. Sun, M. Deyu, C. Yang, A. Cichocki, “Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements”, IEEE Transactions on Image Processing, June 2016.
S. Xia, H. Sun, B. Chen, "A regularized tensor decomposition method with adaptive rank adjustment for Compressed-Sensed-Domain background subtraction", Signal Processing: Image Communication, January 2018.
L. Miao, “Optimization Algorithms on Tensor”, PhD Thesis, The Hong Kong University of Science and Technology, Hong Kong, February 2020.
X. Yu, Z. Luo, “A sparse tensor optimization approach for background subtraction from compressive measurements”, Multimedia Tools and Applications 2021.
M. Mozaffari, P. Markopoulos, "Robust Barron-Loss Tucker Tensor Decomposition", Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, 2021, pages 1651-1655, 2021.
B. Su, “Robust Anomaly Detection via Tensor Chidori Pseudoskeleton Decomposition”, Preprint, 2025.
S. Zhang, X. Wang, “Robust Tensor Decomposition with Kernel Rescaled Error Loss For Feature Extraction And Dimensionality Reduction”, Expert Systems with Applications, Volume 270, April 2025.
3. Tubal Decomposition (1 paper)
F. Zhang, L. Yang, J. Wang, X. Luo, "Randomized sampling techniques based low-tubal-rank plus sparse tensor recovery", Knowledge-Based Systems, Volume 261, February 2023.
4 - MOP Decomposition (1 paper)
Q. Luo, M. Yang, W. Li, M. Xiao, "Multidimensional Data Processing with Bayesian Inference via Structural Block Decomposition", IEEE Transactions on Cybernetics, January 2023.
5. CUR Decomposition (4 papers)
H. Cai, Z. Chao, L. Huang, D. Needell, "Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition", Fourth Workshop on Robust Subspace Learning and Computer Vision, ICCV 2021, Montréal, Canada, October 2021.
H. Cai, Z. Chao, L. Huang, D. Needell, "Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruption", Preprint, 2023.
Z. Chao, “Data Completion and Robust Principal Component Analysis under Low-rank Restrictions”, PhD Thesis, University of California, Los Angeles, USA, 2022.
H. Cai, Z. Chao, L. Huang, D. Needell, “Robust Tensor CUR Decompositions: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruptions”, SIAM Journal on Imaging Sciences, Volume 17, No. 1, pages 225-247, January 2024.
6 - BM Decomposition (4 papers)
F. Tian, M. Kilmer, E. Miller, A. Patra, A. Konstorum, “Approximate tensor BM product decomposition for temporal analysis of third-order data”, SIAM Meeting, 2022
F. Tian, M. Kilmer, E. Miller, A. Patra, "Tensor BM-Decomposition for Compression and Analysis of Spatio-Temporal Third-order Data", AWM Research Symposium, September 2023.
F. Tian, M.Kilmer, E. Miller, A. Patra, "Tensor BM-Decomposition for Compression and Analysis of Video Data”, Preprint, September 2024.
F. Tian, “Computation of Tensor Decompositions in The Bhattacharya–Mesner Algebra with Applications in Data Science”, PhD Thesis, Tufts University, USA, May 2025.
7 - Ring Decomposition (1 paper)
Y. Qiu, G. Zhou, A. Wang, Z. Huang, Q. Zhao, “Towards Multi-Mode Outlier Robust Tensor Ring Decomposition”, AAAI Conference on Artificial Intelligence, AAAI 2024, 2024.
C. Solvers (15 papers)
1- Convex Solvers (7 papers)
Singular Value Decomposition (1 paper)
L. De Lathauwer, B. De Moor, J. Vandewalle, "A multilinear singular value decomposition", SIAM journal on Matrix Analysis and Applications, Volume 21, No. 4, pages, 1253-1278, 2000.
Mixture Augmented Lagrange Multiplier (3 papers)
H. Tan, B. Cheng, J. Feng, G. Feng, Y. Zhang, "Tensor recovery via multilinear augmented Lagrange multiplier method", International Conference on Image and Graphics, ICIG 2011, pages 141–146, August 2011.
H. Tan, B. Cheng, J. Feng, G. Feng, W. Wang, Y. Zhang, "Low-n-rank tensor recovery based on multi-linear augmented Lagrange multiplier method", Neurocomputing, January 2013.
H. Tan, B. Cheng, J. Feng, L. Liu, W. Wang, "Mixture augmented lagrange multiplier method for tensor recovery and its applications", Special Issue on Green Intelligent Transport System, 2013.
Iterative Block Tensor SVT (1 paper)
L. Chen Y. Liu, C. Zhu, "Iterative Block Tensor Singular Value Thresholding for Extraction of Low Rank Component of Image Data", Preprint, 2017.
LRSTS (1 paper)
P. Shah and N. Rao and G. Tan", "Sparse and Low-Rank Tensor Decomposition", Neural Information Processing Systems, NIPS 2015, 2015.
ADMM-TR (1 paper)
H. Tan,G. Feng, J. Feng, W. Wang, Y. Zhang, "Traffic volume data outlier recovery via tensor model", Mathematical Problems in Engineering, 2013.
2 - Non-convex Solvers (4 papers)
A. Anandkumar P. Jain, Y. Shi, U. Niranjan, "Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations", International Conference on Artificial Intelligence and Statistics, AISTATS 2016, 2016.
T. Li, J. Ma, "Non-convex Penalty for Tensor Completion and Robust PCA", Preprint, 2019
J. Xue, Y. Zhao, W. Liao, J. Chan, “Nonconvex tensor rank minimization and its applications to tensor recovery”, Information Sciences, Volume 503, pages 109-128, 2019.
W. Chen, X. Gong, N. Song, "Nonconvex Robust Low-Rank Tensor Reconstruction via an Empirical Bayes Methods”, IEEE Transactions on Signal Processing, Volume 67, No. 22, pages 5785-5797, 2019.
3 - Fast Solvers (3papers)
J. Cai, J. Li, D. Xia, "Generalized Low-rank plus Sparse Tensor Estimation by Fast Riemannian Optimization", Preprint, March 2021.
H. Qiu, Y. Wang, S. Tang, D. Meng, Q. Yao, “Fast and Provable Nonconvex Tensor RPCA”, International Conference on Machine Learning, ICML 2022, July 2022.
Q. Zhu, S Wu, S. Fang, Q. Wu, S. Xie, “Fast Tensor Robust Principal Component Analysis with Estimated Multi-rank and Riemannian Optimization”, Applied Intelligence, Volume 55, 2025.
4 - Unrolled TRPCA (1 paper)
H. Dong, M. Shah, S. Donegan, Y. Chi, "Deep Unfolded Tensor Robust PCA with Self-Supervised Learning”, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Rhodes Island, Greece, 2023.
D. Compressive Sensing Algorithms (7 papers)
W. Cao, Y. Wang, J. Sun, D. Meng, C. Yang, A. Cichocki, Z. Xu, “A Novel Tensor Robust PCA Approach for Background Subtraction from Compressive Measurements”, Preprint, March 2015.
W. Cao, Y. Wang, J. Sun, M. Deyu, C. Yang, A. Cichocki, “Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements”, IEEE Transactions on Image Processing, June 2016.
S. Xia, H. Sun, B. Chen, “A regularized tensor decomposition method with adaptive rank adjustment for compressed-sensed-domain background subtraction”, Signal Processing: Image Communication, January 2018.
L. Han, W. Wei, L. Gao, "Compressed Video Background/Foreground Recovery and Separation Based on PTV-TV Tensor Modeling", Journal of South China University of Technology, Volume 47, Issue 2, Pages 59-67, February 2019.
A. Tim, S. George, "Simultaneous Reconstruction and Moving Object Detection from Compressive Sampled Surveillance Videos", IEEE Transactions on Image Processing, July 2020.
X. Yu, Z. Luo “A sparse tensor optimization approach for background subtraction from compressive measurements”, Multimedia Tools and Applications 2021.
Z. Li, Y. Wang, Q. Zhao, S. Zhang, D. Meng, "A Tensor-Based Online RPCA Model for Compressive Background Subtraction", IEEE Transactions on Neural Networks and Learning Systems, Volume 34, Issue 12, 2023.
E. Randomized Algorithms (1 paper)
N. Erichson, K. Manohar, S. Brunton, J. Kutz, "Randomized CP Tensor Decomposition" ,Preprint, 2017.
F. Spatio-temporal Algorithms (7 papers)
W. Hu, Y. Yang, W. Zhang, Y. Xie, “Moving Object Detection using Tensor Based Low-Rank and Saliently Fused-Sparse Decomposition”, IEEE Transactions on Image Processing, 2016.
Y. Wei, Y. Liu, L. Jia, X. Xiu, J. Liu, "Foreground Extraction via the Tensor-Based RPCA with Non-Convex Fused Sparsity", International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018, pages 624-630, Huangshan, China, 2018.
Z. Zhang, Y. Chang, S. Zhong, L. Yan, X. Zou, "Learning dynamic background for weakly supervised moving object detection", Image and Vision Computing, February 2022.
Q. Yin, T. Liu, Z. Lin, W. An, Y. Guo, “Moving object detection in satellite videos via spatial–temporal tensor model and weighted Schatten p-norm minimization, IEEE Geoscience and Remote Sensing Letters, Volume 19, pages 1–5, 2022.
W. An, Z. Chen, “Incorporating Deep Background Prior Into Model-Based Method for Unsupervised Moving Vehicle Detection in Satellite Videos”, IEEE Transactions On Geoscience and Remote Sensing, 2023.
N. Sabat, S. Raj, S. George, S. Kumar,"A computationally efficient moving object detection technique using tensor QR decomposition based TRPCA framework", Journal of Visual Communication and Image Representation , Volume 92, April 2023.
B. Alawode, S. Javed, “Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction”, IEEE Transactions on Neural Networks and Learning Systems, March 2025.
G. Modified Algorithms (21 papers)
1 -Tensor RPCA (3 papers)
L. Tran, C. Navasca, J. Luo, "Video detection anomaly via low-rank and sparse decompositions", IEEE New York Image Processing Workshop, WNYIPW 2012, pages 17–20, November 2012.
C. Lu, J. Feng, Y. Chen, W. Liu, Z. Lin, S. Yan, "Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
K. Gao, Z. Huang, “Tensor Robust Principal Component Analysis via Tensor Fibered Rank and lp Minimization”, SIAM Journal on Imaging Sciences, Volume 16, Issue 1, pages 423 – 460, 2023.
2 - Graph based TRPCA (3 papers)
N. Shahid, F. Grassi, P. Vandergheynst, "Tensor Robust PCA on Graphs", IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019, pages 5406-5410, 2019.
S. Sofuoglu, S. Aviyente, "Graph Regularized Low-Rank Tensor-Train for Robust Principal Component Analysis”, IEEE Signal Processing Letters, Volume 29, pages1152-1156, 2022.
M. Indibi, S. Aviyente, "Spatiotemporal Group Anomaly Detection via Graph Total Variation on Tensors”, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024, pages 7035-7039 , Seoul, South Korea, 2024.
S. Yuan, H. Wang, “Moving Target Segmentation Method based on Tensor Decomposition and Graph Laplacian Regularization”, Journal of Measurements in Engineering, April 2025.
3 - Sparse Outliers Iterative Removal Algorithm (2 papers)
L. Li, P. Wang, Q. Hu, S. Cai, "A sparse outliers iterative removal algorithm to model the background in the video sequences", Preprint, 2014.
L. Li, P. Wang, Q. Hu, S. Cai, “Efficient Background Modeling Based on Sparse Representation and Outlier Iterative Removal”, IEEE Transactions on Circuits and Systems for Video Technology, December 2014.
4 - Rank-1 Tensor Decomposition (1 paper)
B. Zhou, F. Zhang, L. Peng, “Background modeling for dynamic scenes using tensor decomposition” International Conference on Electronics Information and Emergency Communication, ICEIEC 2016, Beijing, China, pages 206-210, 2016.
5 - t-CUR Decomposition (2 papers)
D. Tarzanagh, G. Michailidis, "Fast Monte Carlo Algorithms for Tensor Operations", Preprint, April 2017.
D. Tarzanagh, G. Michailidis, "Fast Randomized Algorithms for t-Product Based Tensor Operations and Decompositions with Applications to Imaging Data", Preprint, 2018.
6 - KDRSDL Tensor Decomposition (1 paper)
M. Bahri, Y. Panagakis, S. Zafeiriou, "Robust Kronecker Decomposable Component Analysis for Low Rank Modeling", Preprint, 2017.
7 - t-SVD Tensor Decomposition (5 papers)
M. Kilmer, K. Braman, N. Hao, R. Hoover, "Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging", SIAM Journal on Matrix Analysis and Applications, Volume 34, No.1, pages 148–172, 2013.
Z. Zhang, G. Ely, S. Aeron, N. Hao, M. Kilmer, "Novel methods for multilinear data completion and de-noising based on tensor-SVD", IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pages 3842-3849, 2014.
C. Lu, J. Feng, Y. Chen, W. Liu, Z. Lin, S. Yan, "Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization", IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, 2016.
H. Xu, C. Fang, R. Wang, S. Chen, “Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis”, IEEE Transactions on Artificial Intelligence, March 2024.
L. Feng, C. Zhu, Y. Liu, S. Ravishankar, L. Huang, “Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA”, Preprint, 2025.
8 - Cauchy Distribution (1 paper)
Y. Wu, H. Tan, Y. Li, F. Li, H. He, "Robust tensor decomposition based on Cauchy distribution and its applications", Neurocomputing, Volume 223, pages 101-117, Feburary 2017.
9 - Outlier-Robust Tensor PCA (1 paper)
L. Li, P. Wang, Q. Hu, S. Cai, “Efficient Background Modeling Based on Sparse Representation and Outlier Iterative Removal”, IEEE Transactions on Circuits and Systems for Video Technology, December 2014.
10 - Total Variation (3 papers)
Y. Chen, S. Wang, Y. Zhou, "Tensor nuclear norm-based low-rank approximation with total variation regularization”, IEEE Journal of Selected Topics in Signal Processing, December 2018.
L. Chen, Y. Ban, X. Wang, "Background subtraction based on tensor nuclear norm and 3D total variation", Journal of Computer Applications, Volume 40, No. 9, pages 2737-2742, May 2020.
X. Xu, L. Che, X. Wang“Background Subtraction based on Tensor Robust Principal Component Analysis with Side Information”, Signal, Image and Video Processing, May 2025.
H. Norms (25 papers)
Y. Liu, L. Chen, C. Zhu, "Improved Robust Tensor Principal Component Analysis via Low Rank Core Matrix", IEEE Journal of Selected Topics in Signal Processing, December 2018.
D. Driggs, S. Becker, J. Boyd-Graberz, “Tensor Robust Principal Component Analysis: Better recovery with atomic norm regularization”, Preprint, January 2019.
T. Kim, Y. Choe, "Real-time Background Subtraction via L1 Norm Tensor Decomposition", Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018, pages 1963-1967 Honolulu, USA, 2018.
S. Cai, Q. Luo, M. Yang., W. Li, M. Xiao, "Tensor Robust Principal Component Analysis via Non-Convex Low Rank Approximation", MDPI Applied Sciences, 2019.
A. Tom, S. George, “Tensor Total Variation Regularized Moving Object Detection for Surveillance Videos”, IEEE International Conference on Signal Processing and Communications, SPCOM 2018, Bangalore, India, pages 327-331, 2018.
A. Tom, S. George, "A Three-Way Optimization Technique for Noise Robust Moving Object Detection Using Tensor Low-Rank Approximation, l1/2, and TTV Regularizations", IEEE Transactions on Cybernetics, 2019.
L. Chen, J. Liu, X. Wang, “Background subtraction with Kronecker-basis-representation based tensor sparsity and l1,1,2 norm”, Multidimensional Systems and Signal Processing, 2020.
Q. Xie, Q. Zhao, D. Meng, "Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery", IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1888–1902, 2018.
B. Jiang, S. Ma, S. Zhang, "Low-M-Rank Tensor Completion and Robust Tensor PCA", IEEE Journal of Selected Topics in Signal Processing, Volume 12, No. 6, pages 1390-1404, December 2018.
Q. Gao, P. Zhang, W. Xia, D. Xie, X. Gao, D. Tao, "Enhanced Tensor RPCA and its Application”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 43, No. 6, pages 2133-2140, June 2021.
W. Ge, J. Li, X. Wang, "Robust Tensor Principal Component Analysis Based on F-norm", IEEE International Conference on Mechatronics and Automation ICMA 2020, Beijing, China, 2020.
L. Chen, Y. Ban, X. Wang, “Background subtraction based on tensor nuclear and L1,1,2 norm”, Signal, Image and Video Processing, 2021.
Q. Yu, M. Yang, "Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics", Pattern Recognition, Volume 137, May 2023.
R. Fan, M. Jing, J. Shi, L. Li, Z. Wang, “TVRPCA＋: Low-rank and sparse decomposition based on spectral norm and structural sparsity-inducing norm”, Signal Processing, 2023.
Y. Wang, K. Kou, H. Chen, Y. Tang, L. Li, "Double Auto-Weighted Tensor Robust Principal Component Analysis", IEEE Transactions on Image Processing, Volume 32, pages 5114-5125, 2023.
Y. Lu, L. Chen, X. Wang, “A novel non-convex tensor function based TRPCA for foreground detection”, Preprint, 2023.
P Zhang, J. Geng, Y. Liu, S. Yang, “Robust principal component analysis based on tensor train rank and Schatten p-norm”, The Visual Computer, Volume 39, pages 5849-5867, 2023.
Y. Liu, B. Du, Y. Chen, L. Zhang, M. Gong, D. Tao, "Convex–Concave Tensor Robust Principal Component Analysis", International Journal of Computer, 2023.
Z. Zhang, S. Liu, Z. Lin, J. Xue, L. Liu, “A fast correction approach to tensor robust principal component analysis”, Applied Mathematical Modelling, Volume 128, pages 195-219, April 2024.
J. Lin, T. Huang, X. Zhao, T. Ji, Q. Zhao, "Tensor Robust Kernel PCA for Multidimensional Data”, IEEE Transactions on Neural Networks and Learning Systems, 2024.
X. Geng, Q. Guo, S. Hui, M. Yang, C. Zhang, “Tensor robust PCA with nonconvex and nonlocal regularization”, Computer Vision and Image Understanding, March 2024.
T. Yan, Q. Guo, "Tensor Robust Principal Component Analysis via Dual lp Quasi-norm Sparse Constraints", Digital Signal Processing, April 2024.
K. Tang, Y. Fan, Y. Song, “Improvement of robust tensor principal component analysis based on generalized nonconvex approach”, Applied Intelligence, June 2024.
Y. Xu, K. Li, L. Yang, Y. Wen, “Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation”, Preprint, April 2025.
H. Zheng, Y. Lou, G. Tian, C. Wang, “Tensor Robust Principal Component Analysis Via The Tensor Nuclear Over Frobenius Norm”, Preprint, May 2025.
I. Factorization (1 paper)
A. Wang, Z. Jin,· J. Yang, “A faster tensor robust PCA via tensor factorization”, International Journal of Machine Learning and Cybernetics, June 2020.
J. Incremental Algorithms (12 papers)
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.
C. Qiu, X. Wu, H. Xu, "Recursive Projected Sparse Matrix Recovery (ReProCSMR) With Application In Real-Time Video Layer Separation", IEEE International Conference on Image Processing, ICIP 2014, pages 1332-1336, October 2014.
Z. Zhang, D. Liu, A. Vetro,"An online tensor robust PCA algorithm for sequential 2D data", IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2016, 2016.
P. Li, J. Feng, X. Jin, L. Zhang, X. Xu, S. Yan, “Online Robust Low-Rank Tensor Learning”, International Joint Conference on Artificial Intelligence, IJCAI 2017, 2017.
P. Li, J. Feng, X. Jin, L. Zhang, X. Xu, S. Yan, "Online Robust Low-Rank Tensor Modeling for Streaming Data Analysis”, IEEE Transactions on Neural Networks and Learning Systems, 2018.
S. Zhou, “On Dynamic Tensor Decompositions”, PhD Thesis, University of Melbourne, May 2019.
D. Chachlakis, M. Dhanaraj, A. Prater-Bennette, P. Markopoulos, “Dynamic L1-norm Tucker Tensor Decomposition”, Preprint, 2020.
D. Chachlakis, "Theory and Algorithms for Reliable Multimodal Data Analysis, Machine Learning, and Signal Processing", PhD Thesis, Rochester Institute of Technology, May 2021.
A. Rontogiannis, E. Kofidis, P. Giampouras, "Online Rank-Revealing Block-Term Tensor Decomposition", Preprint, June 2021.
M. Salut, D. Anderson, "Online Tensor Robust Principal Component Analysis", IEEE Access, July 2022.
M. Amoozegar, M. Akbarizadeh, T. Bouwmans, “Robust and Efficient FISTA-based Method for Moving Object Detection under Background Movements”, Knowledge-Based Systems, April 2024.
L. Feng, Y. Liu, Z. Liu, C. Zhu, "Online Nonconvex Robust Tensor Principal Component Analysis", IEEE Transactions on Neural Networks and Learning Systems, 2024.
K. Real-Time Algorithms (2 papers)
J. Bin, M. Kang, Z. Liu, "GPU-Accelerated Tensor Decomposition for Moving Object Detection from Multimodal Imaging", IEEE Sensors, pages 1-4, 2020.
A. Muhammad, A. Abdelgawad, P. Jing, R. Cheung, “Randomized Tensor Decomposition using Parallel Reconfigurable Systems”, The Journal of Supercomputing, February 2025.