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In the tensor completion problem, one seeks to estimate a low-rank tensor based on a random sample of revealed entries.
Perturbation bounds in connection with singular value decomposition
P.-Ȧ. Wedin · 1972
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Interior point methods in semidefinite programming with applications to combinatorial optimization
F. Alizadeh · 1995
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Decoupling inequalities for the tail probabilities of multivariate U U -statistics
V. H. de la Peña and S. J. Montgomery-Smith · 1995
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Relations between average case complexity and approximation complexity
U. Feige · 2002
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Exact matrix completion via convex optimization
E. J. Candès and B. Recht · 2009
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The power of convex relaxation: near-optimal matrix completion
E. J. Candès and T. Tao · 2010
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Tensor completion for on-board compression of hyperspectral images
N. Li and B. Li · 2010
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Estimation of low-rank tensors via convex optimization
R. Tomioka, K. Hayashi, and H. Kashima · 2010
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Recovering low-rank matrices from few coefficients in any basis
D. Gross · 2011
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Tensor completion and low-n-rank tensor recovery via convex optimization
S. Gandy, B. Recht, and I. Yamada · 2011
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Applications of tensor (multiway array) factorizations and decompositions in data mining
M. Mørup · 2011
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A simpler approach to matrix completion
B. Recht · 2011
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Tensor versus matrix completion: a comparison with application to spectral data
M. Signoretto, R. Van de Plas, B. De Moor, and J. A. Suykens · 2011
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Statistical performance of convex tensor decomposition
R. Tomioka, T. Suzuki, K. Hayashi, and H. Kashima · 2011
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A spectral algorithm for learning hidden markov models
D. Hsu, S. M. Kakade, and T. Zhang · 2012
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Tensor completion for estimating missing values in visual data
J. Liu, P. Musialski, P. Wonka, and J. Ye · 2013
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Sum-of-squares proofs and the quest toward optimal algorithms
B. Barak and D. Steurer · 2014
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Square deal: Lower bounds and improved convex relaxations for tensor recovery
C. Mu, B. Huang, J. Wright, and D. Goldfarb · 2014
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Tensor prediction, Rademacher complexity and random 3-XOR, 2015
B. Barak and A. Moitra · 2015
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Fast spectral algorithms from sum-of-squares proofs: tensor decomposition and planted sparse vectors
S. B. Hopkins, T. Schramm, J. Shi, and D. Steurer · 2015
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User-friendly tail bounds for sums of random matrices
J. A. Tropp · 2012
Cited alongside, same era.
Introduction to the non-asymptotic analysis of random matrices
R. Vershynin · 2012
Cited alongside, same era.
Tensor completion based on nuclear norm minimization for 5d seismic data reconstruction
N. Kreimer, A. Stanton, and M. D. Sacchi · 2013
Cited alongside, same era.
On tensor completion via nuclear norm minimization
M. Yuan and C.-H. Zhang · 2015
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Nuclear norm of higher-order tensors
S. Friedland and L.-H. Lim · 2016
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Incoherent tensor norms and their applications in higher order tensor completion
M. Yuan and C.-H. Zhang · 2016
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