B. Recht, Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization, SIAM Review, vol.52, issue.3, pp.471-501, 2010.
DOI : 10.1137/070697835

J. Liu, Tensor Completion for Estimating Missing Values in Visual Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.1, pp.208-220, 2013.
DOI : 10.1109/TPAMI.2012.39

N. Kreimer and M. D. Sacchi, A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation, GEOPHYSICS, vol.77, issue.3, pp.113-122, 2012.
DOI : 10.1190/geo2011-0399.1

M. Signoretto, Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data, IEEE Signal Processing Letters, vol.18, issue.7, pp.403-406, 2011.
DOI : 10.1109/LSP.2011.2151856

B. Romera-paredes, Multilinear multitask learning, Proc. 30th Int. Conf. Mach. Learning, pp.1444-1452, 2013.

C. J. Hillar and L. Lim, Most Tensor Problems Are NP-Hard, Journal of the ACM, vol.60, issue.6, pp.1-45
DOI : 10.1145/2512329
URL : http://arxiv.org/abs/0911.1393

T. G. Kolda and B. W. Bader, Tensor Decompositions and Applications, SIAM Review, vol.51, issue.3, pp.455-500, 2009.
DOI : 10.1137/07070111X
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.130.782

L. Grasedyck, A literature survey of low-rank tensor approximation techniques, GAMM-Mitteilungen, vol.93, issue.3, pp.53-78, 2013.
DOI : 10.1002/gamm.201310004

S. Oymak, Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices, IEEE Transactions on Information Theory, vol.61, issue.5, 2012.
DOI : 10.1109/TIT.2015.2401574
URL : http://arxiv.org/abs/1212.3753

R. Tomioka, Estimation of low-rank tensors via convex optimization, 2010.

C. Mu, B. Huang, J. Wright, and D. Goldfarb, Square deal: Lower bounds and improved relaxations for tensor recovery, Proc. 31st Int. Conf. Mach. Learning, pp.73-81, 2014.

]. A. Uschmajew, A new convergence proof for the high-order power method and generalizations, 2014.

H. Rauhut, Low rank tensor recovery via iterative hard thresholding, Proc. 10th Int. Conf. Sampling Theory Applicat, 2013.
DOI : 10.1016/j.laa.2017.02.028
URL : http://arxiv.org/abs/1602.05217

T. Blumensath and M. E. Davies, Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance, IEEE Journal of Selected Topics in Signal Processing, vol.4, issue.2, pp.298-309, 2010.
DOI : 10.1109/JSTSP.2010.2042411

G. Allaire, Numerical Analysis and Optimization, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01242950

M. Ishteva, Best Low Multilinear Rank Approximation of Higher-Order Tensors, Based on the Riemannian Trust-Region Scheme, SIAM Journal on Matrix Analysis and Applications, vol.32, issue.1, pp.115-135, 2011.
DOI : 10.1137/090764827

C. F. Caiafa and A. Cichocki, Stable, Robust, and Super Fast Reconstruction of Tensors Using Multi-Way Projections, IEEE Transactions on Signal Processing, vol.63, issue.3, pp.780-793, 2015.
DOI : 10.1109/TSP.2014.2385040