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Low-rank tensor completion problem aims to recover a tensor from limited observations, which has many real-world applications.
Variable selection via nonconcave penalized likelihood and its oracle properties
Fan, J. and Li, R · 2001
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Multilinear analysis of image ensembles: Tensorfaces
Vasilescu, A. and Terzopoulos, D · 2002
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Efficient MATLAB computations with sparse and factored tensors
Bader, B. and Kolda, T · 2007
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The proximal average: basic theory
Bauschke, H., Goebel, R., Lucet, Y., and Wang, X · 2008
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Enhancing sparsity by reweighted ℓ 1 \ell_{1} minimization
Candès, E., Wakin, M., and Boyd, S · 2008
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Convex optimization
Boyd, S. and Vandenberghe, L · 2009
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Exact matrix completion via convex optimization
Candès, E. and Recht, B · 2009
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Tensor decompositions and applications
Kolda, T. and Bader, B · 2009
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Uncoverning groups via heterogeneous interaction analysis
Lei, T., Wang, X., and Liu, H · 2009
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A singular value thresholding algorithm for matrix completion
Cai, J.-F., Candès, E., and Shen, Z · 2010
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Spectral regularization algorithms for learning large incomplete matrices
Mazumder, R., Hastie, T., and Tibshirani, R · 2010
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Pairwise interaction tensor factorization for personalized tag recommendation
Rendle, S. and Schmidt-Thieme, L · 2010
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Estimation of low-rank tensors via convex optimization
Tomioka, R., Hayashi, K., and Kashima, H · 2010
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Scalable tensor factorizations for incomplete data
Acar, E., Dunlavy, D., Kolda, T., and Mørup, M · 2011
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Multi-relational link prediction in heterogeneous information networks
Davis, D., Lichtenwalter, R., and Chawla, N. V · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y · 2011
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Tensor completion and low-n-rank tensor recovery via convex optimization
Gandy, S., Recht, B., and Yamada, I · 2011
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Tensor-train decomposition
Oseledets, I · 2011
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Tensor versus matrix completion: a comparison with application to spectral data
Signoretto, M., Van de Plas, R., De Moor, B., and Suykens, J · 2011
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Statistical performance of convex tensor decomposition
Tomioka, R., Suzuki, T., Hayashi, K., and Kashima, H · 2011
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The convex geometry of linear inverse problems
Chandrasekaran, V., Recht, B., Parrilo, P., and Willsky, A · 2012
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Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss-Seidel methods
Attouch, H., Bolte, J., and Svaiter, B · 2013
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Most tensor problems are NP-hard
Hillar, C. and Lim, L.-H · 2013
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Fast and accurate matrix completion via truncated nuclear norm regularization
Hu, Y., Zhang, D., Ye, J., Li, X., and He, X · 2013
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Tensor completion for estimating missing values in visual data
Liu, J., Musialski, P., Wonka, P., and Ye, J · 2013
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Introductory lectures on convex optimization: A basic course
Nesterov, Y · 2013
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
Ghadimi, S. and Lan, G · 2016
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Towards faster rates and oracle property for low-rank matrix estimation
Gui, H., Han, J., and Gu, Q · 2016
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Low-rank tensor completion: a Riemannian manifold preconditioning approach
Kasai, H. and Mishra, B · 2016
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Nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm
Lu, C., Tang, J., Yan, S., and Lin, Z · 2016
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Matrix and Tensor Factorization Techniques for Recommender Systems
Symeonidis, P. and Zioupos, A · 2016
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Efficient tensor completion for color image and video recovery: Low-rank tensor train
Bengua, J., Phien, H., Tuan, H., and Do, M · 2017
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Proximal algorithms
Parikh, N. and Boyd, S · 2013
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Convex tensor decomposition via structured schatten norm regularization
Tomioka, R. and Suzuki, T · 2013
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Parallel matrix factorization for low-rank tensor completion
Xu, Y., Hao, R., Yin, W., and Su, Z · 2013
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Better approximation and faster algorithm using the proximal average
Yu, Y.-L · 2013
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Fast multivariate spatio-temporal analysis via low rank tensor learning
Bahadori, M., Yu, Q., and Liu, Y · 2014
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Proximal alternating linearized minimization or nonconvex and nonsmooth problems
Bolte, J., Sabach, S., and Teboulle, M · 2014
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Weighted nuclear norm minimization and its applications to low level vision
Gu, S., Xie, Q., Meng, D., Zuo, W., Feng, X., and Zhang, L · 2017
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Efficient sparse low-rank tensor completion using the Frank-Wolfe algorithm
Guo, X., Yao, Q., and Kwok, J · 2017
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Convergence analysis of proximal gradient with momentum for nonconvex optimization
Li, Q., Zhou, Y., Liang, Y., and Varshney, P · 2017
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Tensors for data mining and data fusion: Models, applications, and scalable algorithms
Papalexakis, E., Faloutsos, C., and Sidiropoulos, N · 2017
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Low rank tensor recovery via iterative hard thresholding
Rauhut, H., Schneider, R., and Stojanac, Ž · 2017
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Tensor completion algorithms in big data analytics
Song, Q., Ge, H., Caverlee, J., and Hu, X · 2017
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Knowledge graph completion via complex tensor factorization
Trouillon, T., Dance, C. R., Gaussier, É., Welbl, J., Riedel, S., and Bouchard, G · 2017
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Efficient inexact proximal gradient algorithm for nonconvex problems
Yao, Q., Kwok, J., Gao, F., Chen, W., and Liu, T.-Y · 2017
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Exact tensor completion using t-SVD
Zhang, Z. and Aeron, S · 2017
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Canonical tensor decomposition for knowledge base completion
Lacroix, T., Usunier, N., and Obozinski, G · 2018
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A dual framework for low-rank tensor completion
Nimishakavi, M., Jawanpuria, P., and Mishra, B · 2018
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Efficient convex completion of coupled tensors using coupled nuclear norms
Wimalawarne, K. and Mamitsuka, H · 2018
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Large-scale low-rank matrix learning with nonconvex regularizers
Yao, Q., Kwok, J., Wang, T., and Liu, T.-Y · 2018
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