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Low-rank tensor decomposition generalizes low-rank matrix approximation and is a powerful technique for discovering low-dimensional structure in high-dimensional data.
Fast Monte Carlo algorithms for matrices I: Approximating matrix multiplication
Drineas, P., Kannan, R., and Mahoney, M. W. (2006) · 2006
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Practical leverage-based sampling for low-rank tensor decomposition
Larsen, B. W. and Kolda, T. G. (2020) · 2006
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Tensor decompositions, alternating least squares and other tales
Comon, P., Luciani, X., and De Almeida, A. L. (2009) · 2009
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Tensor decompositions and applications
Kolda, T. G. and Bader, B. W. (2009) · 2009
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Scalable tensor factorizations for incomplete data
Acar, E., Dunlavy, D. M., Kolda, T. G., and Mørup, M. (2011) · 2011
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Faster least squares approximation
Drineas, P., Mahoney, M. W., Muthukrishnan, S., and Sarlós, T. (2011) · 2011
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Maximum block improvement and polynomial optimization
Chen, B., He, S., Li, Z., and Zhang, S. (2012) · 2012
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Tensor completion for estimating missing values in visual data
Liu, J., Musialski, P., Wonka, P., and Ye, J. (2012) · 2012
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Most tensor problems are NP-hard
Hillar, C. J. and Lim, L.-H. (2013) · 2013
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Low-rank matrix completion using alternating minimization
Jain, P., Netrapalli, P., and Sanghavi, S. (2013) · 2013
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Iterative row sampling
Li, M., Miller, G. L., and Peng, R. (2013) · 2013
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Tensor regression with applications in neuroimaging data analysis
Zhou, H., Li, L., and Zhu, H. (2013) · 2013
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Provable tensor factorization with missing data
Jain, P. and Oh, S. (2014) · 2014
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Low-rank tensor completion by Riemannian optimization
Kressner, D., Steinlechner, M., and Vandereycken, B. (2014) · 2014
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Sketching as a Tool for Numerical Linear Algebra
Woodruff, D. P. (2014) · 2014
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Fast randomized kernel ridge regression with statistical guarantees
Alaoui, A. and Mahoney, M. W. (2015) · 2015
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Uniform sampling for matrix approximation
Cohen, M. B., Lee, Y. T., Musco, C., Musco, C., Peng, R., and Sidford, A. (2015) · 2015
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Tucker factorization with missing data with application to low- n n -rank tensor completion
Filipović, M. and Jukić, A. (2015) · 2015
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Escaping from saddle points—Online stochastic gradient for tensor decomposition
Ge, R., Huang, F., Jin, C., and Yuan, Y. (2015) · 2015
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) · 2017
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An iterative, sketching-based framework for ridge regression
Chowdhury, A., Yang, J., and Drineas, P. (2018) · 2018
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Sketching for kronecker product regression and p-splines
Diao, H., Song, Z., Sun, W., and Woodruff, D. (2018) · 2018
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Low-rank Tucker decomposition of large tensors using TensorSketch
Malik, O. A. and Becker, S. (2018) · 2018
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Ridge regression and provable deterministic ridge leverage score sampling
McCurdy, S. (2018) · 2018
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A dual framework for low-rank tensor completion
Nimishakavi, M., Jawanpuria, P. K., and Mishra, B. (2018) · 2018
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Variants of alternating least squares tensor completion in the tensor train format
Grasedyck, L., Kluge, M., and Kramer, S. (2015) · 2015
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SPALS: Fast alternating least squares via implicit leverage scores sampling
Cheng, D., Peng, R., Liu, Y., and Perros, I. (2016) · 2016
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Low-rank tensor completion: a Riemannian manifold preconditioning approach
Kasai, H. and Mishra, B. (2016) · 2016
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Learning from multiway data: Simple and efficient tensor regression
Yu, R. and Liu, Y. (2016) · 2016
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Input sparsity time low-rank approximation via ridge leverage score sampling
Cohen, M. B., Musco, C., and Musco, C. (2017) · 2017
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Recursive sampling for the Nyström method
Musco, C. and Musco, C. (2017) · 2017
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Optimal sketching for kronecker product regression and low rank approximation
Diao, H., Jayaram, R., Song, Z., Sun, W., and Woodruff, D. (2019) · 2019
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Tensorly: Tensor learning in Python
Kossaifi, J., Panagakis, Y., Anandkumar, A., and Pantic, M. (2019) · 2019
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Towards a unified analysis of random Fourier features
Li, Z., Ton, J.-F., Oglic, D., and Sejdinovic, D. (2019) · 2019
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Singleshot : a scalable tucker tensor decomposition
Traore, A., Berar, M., and Rakotomamonjy, A. (2019) · 2019
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Optimization landscape of tucker decomposition
Frandsen, A. and Ge, R. (2020) · 2020
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Tensor completion made practical
Liu, A. and Moitra, A. (2020) · 2020
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A refined laser method and faster matrix multiplication
Alman, J. and Williams, V. V. (2021) · 2021
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