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Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics.
The expression of a tensor or a polyadic as a sum of products
F. L. Hitchcock · 1927
Earlier work this paper cites.
Symmetric gauge functions and unitarily invariant norms
L. Mirsky · 1960
Earlier work this paper cites.
Some mathematical notes on three-mode factor analysis
L. R. Tucker · 1966
Earlier work this paper cites.
Foundations of the PARAFAC procedure: models and conditions for an explanatory multimodal factor analysis
R. A. Harshman · 1970
Earlier work this paper cites.
Discarding variables in a principal component analysis. I: Artificial data
I. T. Jolliffe · 1972
Earlier work this paper cites.
CANDELINC: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters
J. D. Carroll, S. Pruzansky, and J. B. Kruskal · 1980
Earlier work this paper cites.
Slowly converging PARAFAC sequences: swamps and two-factor degeneracies
B. C. Mitchell and D. S. Burdick · 1994
Earlier work this paper cites.
Efficient algorithms for computing a strong rank-revealing QR factorization
M. Gu and S. C. Eisenstat · 1996
Earlier work this paper cites.
Improving the speed of multi-way algorithms: Part I. Tucker3
C. A. Andersson and R. Bro · 1998
Earlier work this paper cites.
Improving the speed of multiway algorithms: Part II: Compression
R. Bro and C. A. Andersson · 1998
Earlier work this paper cites.
A multilinear singular value decomposition
L. De Lathauwer, B. De Moor, and J. Vandewalle · 2000
Earlier work this paper cites.
On the best rank-1 and rank-(r1, r2,…, rn) approximation of higher-order tensors
L. De Lathauwer, B. De Moor, and J. Vandewalle · 2000
Earlier work this paper cites.
Exact matrix completion via convex optimization
E. J. Candès and B. Recht · 2009
Earlier work this paper cites.
Tensor decompositions and applications
T. G. Kolda and B. W. Bader · 2009
Earlier work this paper cites.
Faster least squares approximation
P. Drineas, M. W. Mahoney, S. Muthukrishnan, and T. Sarlós · 2011
Earlier work this paper cites.
Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
N. Halko, P.-G. Martinsson, and J. A. Tropp · 2011
Earlier work this paper cites.
Randomized algorithms for matrices and data
M. W. Mahoney · 2011
Earlier work this paper cites.
Fast approximation of matrix coherence and statistical leverage
P. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. P. Woodruff · 2012
Earlier work this paper cites.
Tensor hypercontraction density fitting. I. Quartic scaling second-and third-order Møller-Plesset perturbation theory
E. G. Hohenstein, R. M. Parrish, and T. J. Martínez · 2012
Earlier work this paper cites.
Compressed matrix multiplication
R. Pagh · 2013
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Tensor decompositions for learning latent variable models
A. Anandkumar, R. Ge, D. Hsu, S. M. Kakade, and M. Telgarsky · 2014
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Provable deterministic leverage score sampling
D. Papailiopoulos, A. Kyrillidis, and C. Boutsidis · 2014
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Sketching as a tool for numerical linear algebra
D. P. Woodruff · 2014
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Decomposition of big tensors with low multilinear rank
G. Zhou, A. Cichocki, and S. Xie · 2014
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Communication-optimal distributed principal component analysis in the column-partition model
Accelerating alternating least squares for tensor decomposition by pairwise perturbation
L. Ma and E. Solomonik · 2018
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Low-rank Tucker decomposition of large tensors using Tensorsketch
O. A. Malik and S. Becker · 2018
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Scalable Tucker factorization for sparse tensors-algorithms and discoveries
S. Oh, N. Park, S. Lee, and U. Kang · 2018
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Approximate tensor-product preconditioners for very high order discontinuous Galerkin methods
W. Pazner and P.-O. Persson · 2018
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Tensor random projection for low memory dimension reduction
Y. Sun, Y. Guo, J. A. Tropp, and M. Udell · 2018
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Randomized algorithms for the approximations of Tucker and the tensor train decompositions
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C. Boutsidis and D. Woodruff · 2015
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SPLATT: Efficient and parallel sparse tensor-matrix multiplication
S. Smith, N. Ravindran, N. D. Sidiropoulos, and G. Karypis · 2015
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A practical guide to randomized matrix computations with MATLAB implementations
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Sharper bounds for regularized data fitting
H. Avron, K. L. Clarkson, and D. P. Woodruff · 2016
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SPALS: Fast alternating least squares via implicit leverage scores sampling
D. Cheng, R. Peng, Y. Liu, and I. Perros · 2016
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High performance parallel algorithms for the Tucker decomposition of sparse tensors
O. Kaya and B. Uçar · 2016
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Iterative Hessian sketch: fast and accurate solution approximation for constrained least-squares
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M. Che and Y. Wei · 2019
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Faster Johnson-Lindenstrauss transforms via Kronecker products
R. Jin, T. G. Kolda, and R. Ward · 2019
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Load-balanced sparse MTTKRP on GPUs
I. Nisa, J. Li, A. Sukumaran-Rajam, R. Vuduc, and P. Sadayappan · 2019
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Relative error tensor low rank approximation
Z. Song, D. P. Woodruff, and P. Zhong · 2019
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Adaptive sketching for fast and convergent canonical polyadic decomposition
K. S. Aggour, A. Gittens, and B. Yener · 2020
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Randomized CP tensor decomposition
N. B. Erichson, K. Manohar, S. L. Brunton, and J. N. Kutz · 2020
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Practical leverage-based sampling for low-rank tensor decomposition
B. W. Larsen and T. G. Kolda · 2020
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SGD_Tucker: A novel stochastic optimization strategy for parallel sparse Tucker decomposition
H. Li, Z. Li, K. Li, J. S. Rellermeyer, L. Chen, and K. Li · 2020
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Efficient parallel CP decomposition with pairwise perturbation and multi-sweep dimension tree
L. Ma and E. Solomonik · 2020
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Randomized algorithms for low-rank tensor decompositions in the Tucker format
R. Minster, A. K. Saibaba, and M. E. Kilmer · 2020
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Low-rank Tucker approximation of a tensor from streaming data
Y. Sun, Y. Guo, C. Luo, J. Tropp, and M. Udell · 2020
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Randomized algorithms for computation of Tucker decomposition and higher order SVD (HOSVD)
S. Ahmadi-Asl, S. Abukhovich, M. G. Asante-Mensah, A. Cichocki, A. H. Phan, T. Tanaka, and I. Oseledets · 2021
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Randomized algorithms for the low multilinear rank approximations of tensors
M. Che, Y. Wei, and H. Yan · 2021
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