Fetching the paper…
Reading the bibliography…
This work studies the combinatorial optimization problem of finding an optimal core tensor shape, also called multilinear rank, for a size-constrained Tucker decomposition.
The approximation of one matrix by another of lower rank
Eckart, C. and Young, G · 1936
Earlier work this paper cites.
Fast approximation algorithms for knapsack problems
Lawler, E. L · 1977
Earlier work this paper cites.
Computers and Intractability
Garey, M. R. and Johnson, D. S · 1979
Earlier work this paper cites.
Computational complexity of approximation algorithms for combinatorial problems
Gens, G. V. and Levner, E. V · 1979
Earlier work this paper cites.
The multiple-choice knapsack problem
Sinha, P. and Zoltners, A. A · 1979
Earlier work this paper cites.
On the existence of fast approximation schemes
Korte, B. and Schrader, R · 1981
Earlier work this paper cites.
A minimal algorithm for the multiple-choice knapsack problem
Pisinger, D · 1995
Earlier work this paper cites.
Columbia object image library (COIL-20)
Nene, S. A., Nayar, S. K., Murase, H., et al · 1996
Earlier work this paper cites.
k k -means++: The advantages of careful seeding
Arthur, D. and Vassilvitskii, S · 2006
Earlier work this paper cites.
A Newton–Grassmann method for computing the best multilinear rank- ( r 1 , r 2 , r 3 ) (r_{1},r_{2},r_{3}) approximation of a tensor
Eldén, L. and Savas, B · 2009
Earlier work this paper cites.
Differential-geometric newton method for the best rank- ( r 1 , r 2 , r 3 ) (r_{1},r_{2},r_{3}) approximation of tensors
Ishteva, M., De Lathauwer, L., Absil, P.-A., and Van Huffel, S · 2009
Earlier work this paper cites.
Tensor decompositions and applications
Kolda, T. G. and Bader, B. W · 2009
Earlier work this paper cites.
Breaking the curse of dimensionality, or how to use svd in many dimensions
Oseledets, I. V. and Tyrtyshnikov, E. E · 2009
Earlier work this paper cites.
Hierarchical singular value decomposition of tensors
Grasedyck, L · 2010
Earlier work this paper cites.
Best low multilinear rank approximation of higher-order tensors, based on the Riemannian trust-region scheme
Ishteva, M., Absil, P.-A., Van Huffel, S., and De Lathauwer, L · 2011
Earlier work this paper cites.
Tensor-train decomposition
Oseledets, I. V · 2011
Earlier work this paper cites.
A new truncation strategy for the higher-order singular value decomposition
Vannieuwenhoven, N., Vandebril, R., and Meerbergen, K · 2012
Cited alongside, same era.
Most tensor problems are NP-hard
Hillar, C. J. and Lim, L.-H · 2013
Cited alongside, same era.
Jacobi algorithm for the best low multilinear rank approximation of symmetric tensors
Ishteva, M., Absil, P.-A., and Van Dooren, P · 2013
Cited alongside, same era.
New approaches to multi-objective optimization
Grandoni, F., Ravi, R., Singh, M., and Zenklusen, R · 2014
Cited alongside, same era.
Decomposition of big tensors with low multilinear rank
Zhou, G., Cichocki, A., and Xie, S · 2014
Cited alongside, same era.
Traffic forecasting in complex urban networks: Leveraging big data and machine learning
Singleshot: A scalable Tucker tensor decomposition
Traoré, A., Berar, M., and Rakotomamonjy, A · 2019
Later among the works it cites.
Array programming with numpy
Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al · 2020
Later among the works it cites.
Adaptive tensor learning with tensor networks
Hashemizadeh, M., Liu, M., Miller, J., and Rabusseau, G · 2020
Later among the works it cites.
Tree tensor networks, associated singular values and high-dimensional approximation
Krämer, S · 2020
Later among the works it cites.
Low-rank Tucker approximation of a tensor from streaming data
Sun, Y., Guo, Y., Luo, C., Tropp, J., and Udell, M · 2020
Later among the works it cites.
Adaptive hierarchical subtensor partitioning for tensor compression
Ehrlacher, V., Grigori, L., Lombardi, D., and Song, H · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Schimbinschi, F., Nguyen, X. V., Bailey, J., Leckie, C., Vu, H., and Kotagiri, R · 2015
Cited alongside, same era.
Spals: Fast alternating least squares via implicit leverage scores sampling
Cheng, D., Peng, R., Liu, Y., and Perros, I · 2016
Cited alongside, same era.
Compression of deep convolutional neural networks for fast and low power mobile applications
Kim, Y.-D., Park, E., Yoo, S., Choi, T., Yang, L., and Shin, D · 2016
Cited alongside, same era.
Spatial distributions of local illumination color in natural scenes
Nascimento, S. M., Amano, K., and Foster, D. H · 2016
Cited alongside, same era.
Tensor decomposition for signal processing and machine learning
Sidiropoulos, N. D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E. E., and Faloutsos, C · 2017
Cited alongside, same era.
A practical randomized CP tensor decomposition
Battaglino, C., Ballard, G., and Kolda, T. G · 2018
Cited alongside, same era.
Low-rank Tucker decomposition of large tensors using TensorSketch
Malik, O. A. and Becker, S · 2018
Cited alongside, same era.
Later among the works it cites.
Fast and memory-efficient Tucker decomposition for answering diverse time range queries
Jang, J.-G. and Kang, U · 2021
Later among the works it cites.
Fast and accurate randomized algorithms for low-rank tensor decompositions
Ma, L. and Solomonik, E · 2021
Later among the works it cites.
A rank-adaptive higher-order orthogonal iteration algorithm for truncated tucker decomposition
Xiao, C. and Yang, C · 2021
Later among the works it cites.
Tensor toolbox for MATLAB, version 3.4
Bader, B. W. and Kolda, T. G · 2022
Later among the works it cites.
A Krylov–Schur-like method for computing the best rank- ( r 1 , r 2 , r 3 ) (r_{1},r_{2},r_{3}) approximation of large and sparse tensors
Eldén, L. and Dehghan, M · 2022
Later among the works it cites.
Subquadratic kronecker regression with applications to tensor decomposition
Fahrbach, M., Fu, G., and Ghadiri, M · 2022
Later among the works it cites.
Optimization landscape of Tucker decomposition
Frandsen, A. and Ge, R · 2022
Later among the works it cites.
Practical leverage-based sampling for low-rank tensor decomposition
Larsen, B. W. and Kolda, T. G · 2022
Later among the works it cites.
More efficient sampling for tensor decomposition with worst-case guarantees
Malik, O. A · 2022
Later among the works it cites.