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Tensor completion recovers a multi-dimensional array from a limited number of measurements.
M. F. Barnsley, L. P. Hurd, Fractal image compression, Vol. 1, AK peters Wellesley, 1993
1993
Earlier work this paper cites.
J. I. Latorre, Image compression and entanglement, arXiv preprint quant-ph/0510031 (2005)
2005
Earlier work this paper cites.
T. G. Kolda, B. W. Bader, Tensor decompositions and applications, SIAM Review 51 (3) (2009) 455–500
2009
Earlier work this paper cites.
E. J. Candès, B. Recht, Exact matrix completion via convex optimization, Foundations of Computational mathematics 9 (6) (2009) 717
2009
Earlier work this paper cites.
M. Signoretto, R. Van de Plas, B. De Moor, J. A. Suykens, Tensor versus matrix completion: a comparison with application to spectral data, IEEE Signal Processing Letters 18 (7) (2011) 403–406
2011
Earlier work this paper cites.
S. Gandy, B. Recht, I. Yamada, Tensor completion and low-n-rank tensor recovery via convex optimization, Inverse Problems 27 (2) (2011) 025010
2011
Earlier work this paper cites.
R. Tomioka, T. Suzuki, K. Hayashi, H. Kashima, Statistical performance of convex tensor decomposition, in: Advances in neural information processing systems, 2011, pp. 972–980
2011
Earlier work this paper cites.
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, et al., Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends® in Machine learning 3 (1) (2011) 1–122
2011
Earlier work this paper cites.
J. Liu, P. Musialski, P. Wonka, J. Ye, Tensor completion for estimating missing values in visual data, IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (1) (2013) 208–220
2013
Earlier work this paper cites.
C. J. Hillar, L.-H. Lim, Most tensor problems are NP-hard, Journal of the ACM 60 (6) (2013) 45
2013
Earlier work this paper cites.
M. E. Kilmer, K. Braman, N. Hao, R. C. Hoover, Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging, SIAM Journal on Matrix Analysis and Applications 34 (1) (2013) 148–172
2013
Earlier work this paper cites.
C. Mu, B. Huang, J. Wright, D. Goldfarb, Square deal: Lower bounds and improved relaxations for tensor recovery, in: International Conference on Machine Learning, 2014, pp. 73–81
2014
Cited alongside, same era.
A. Cichocki, D. Mandic, L. De Lathauwer, G. Zhou, Q. Zhao, C. Caiafa, H. A. Phan, Tensor decompositions for signal processing applications: from two-way to multiway component analysis, IEEE Signal Processing Magazine 32 (2) (2015) 145–163
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Q. Zhao, L. Zhang, A. Cichocki, Bayesian CP factorization of incomplete tensors with automatic rank determination, IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (9) (2015) 1751–1763
2015
Cited alongside, same era.
2017
Later among the works it cites.
W. Wang, V. Aggarwal, S. Aeron, Efficient low rank tensor ring completion, in: Computer Vision (ICCV), 2017 IEEE International Conference on, IEEE, 2017
2017
Later among the works it cites.
K. Ye, L.-H. Lim, Tensor network ranks, arXiv preprint arXiv:1801.02662 (2018)
2018
Later among the works it cites.
L. Yuan, J. Cao, X. Zhao, Q. Wu, Q. Zhao, Higher-dimension tensor completion via low-rank tensor ring decomposition, in: 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE, 2018, pp. 1071–1076
2018
Later among the works it cites.
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A. Cichocki, N. Lee, I. Oseledets, A.-H. Phan, Q. Zhao, D. P. Mandic, et al., Tensor networks for dimensionality reduction and large-scale optimization: part 1 low-rank tensor decompositions, Foundations and Trends® in Machine Learning 9 (4-5) (2016) 249–429
2016
Cited alongside, same era.
C. Lu, A Library of ADMM for Sparse and Low-rank Optimization, National University of Singapore, https://github.com/canyilu/LibADMM (June 2016)
2016
Cited alongside, same era.
L. Warnke, On the method of typical bounded differences, Combinatorics, Probability and Computing 25 (2) (2016) 269–299
2016
Cited alongside, same era.
N. D. Sidiropoulos, L. De Lathauwer, X. Fu, K. Huang, E. E. Papalexakis, C. Faloutsos, Tensor decomposition for signal processing and machine learning, IEEE Transactions on Signal Processing 65 (13) (2017) 3551–3582
2017
Cited alongside, same era.
Z. Zhang, S. Aeron, Exact tensor completion using t-SVD, IEEE Transactions on Signal Processing 65 (6) (2017) 1511–1526
2017
Cited alongside, same era.
J. A. Bengua, H. N. Phien, H. D. Tuan, M. N. Do, Efficient tensor completion for color image and video recovery: low-rank tensor train, IEEE Transactions on Image Processing 26 (5) (2017) 2466–2479
2017
Cited alongside, same era.
W. He, L. Yuan, N. Yokoya, Total-variation-regularized tensor ring completion for remote sensing image reconstruction, in: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019, pp. 8603–8607
2019
Closest in time.
Y. Liu, Z. Long, C. Zhu, Image completion using low tensor tree rank and total variation minimization, IEEE Transactions on Multimedia 21 (2) (2019) 338–350
2019
Closest in time.
L. Yuan, C. Li, D. Mandic, J. Cao, Q. Zhao, Tensor ring decomposition with rank minimization on latent space: An efficient approach for tensor completion, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 9151–9158
2019
Closest in time.
J. Yu, C. Li, Q. Zhao, G. Zhao, Tensor-ring nuclear norm minimization and application for visual: Data completion, in: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019, pp. 3142–3146
2019
Closest in time.
2019
Closest in time.
E. J. Candès, T. Tao, The power of convex relaxation: Near-optimal matrix completion, IEEE Transactions on Information Theory 56 (5) (2010) 2053–2080
2080
Closest in time.