W. Austin, G. Ballard, and T. G. Kolda, Parallel tensor compression for large-scale scientific data, IEEE International Parallel and Distributed Processing Symposium, 2016.

G. Ballard, A. Klin, and T. G. Kolda, Tuckermpi: A parallel c++/mpi software package for large-scale data compression via the tucker tensor decomposition. arxiv, 2019.

B. Muthu-manikandan-baskaran, N. Meister, R. Vasilache, and . Lethin, Efficient and scalable computations with sparse tensors, IEEE Conference on High Performance Extreme Computing, pp.1-6, 2012.

S. Becker and M. Osman-asif, Low-rank tucker decomposition of large tensors using tensorsketch, Advances in Neural Information Processing Systems, pp.10117-10127, 2018.

C. F. Caiafa and A. Cichocki, Generalizing the column-row matrix decomposition to multiway arrays, Linear Algebra and its Applications, vol.433, pp.557-573, 2010.

R. B. Cattell, Parallel proportional profiles" and other principles for determining the choice of factors by rotation, Psychometrika, vol.9, issue.4, pp.267-283, 1944.

T. Venkatesan, . Chakaravarthy, W. Jee, D. J. Choi, P. Joseph et al., On optimizing distributed tucker decomposition for sparse tensors, Proceedings of the 2018 International Conference on Supercomputing, pp.374-384, 2018.

M. Che and Y. Wei, Randomized algorithms for the approximations of tucker and the tensor train decompositions, Advances in Computational Mathematics, pp.1-34, 2018.

D. Choi, J. Jang, and U. Kang, Fast, accurate, and scalable method for sparse coupled matrix-tensor factorization, 2017.

D. Choi and L. Sael, Snect: Scalable network constrained tucker decomposition for integrative multi-platform data analysis, 2017.

A. Cichocki and R. Zdunek, Anh Huy Phan, and Shun-ichi Amari. Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation, 2009.

A. Dan, The convergence of sparsified gradient methods, Advances in Neural Information Processing Systems, pp.5977-5987, 2018.

P. Drineas and M. W. Mahoney, A randomized algorithm for a tensor-based generalization of the singular value decomposition, Linear Algebra and its Applications, vol.420, pp.553-571, 2007.

D. Erdös and P. Miettinen, Walk 'n' merge: A scalable algorithm for boolean tensor factorization, IEEE 13th International Conference on Data Mining, pp.1037-1042, 2013.

F. , M. Harper, and J. A. Konstan, The movielens datasets: History and context, TiiS, vol.5, pp.1-19, 2015.

F. L. Hitchcock, The expression of a tensor or a polyadic as a sum of products, J. Math.Phys, vol.6, issue.1, pp.164-189, 1927.

I. Jeon, E. E. Papalexakis, U. Kang, and C. Faloutsos, Haten2: Billionscale tensor decompositions, IEEE 31st International Conference on Data Engineering, pp.1047-1058, 2015.

O. Kaya and B. Uçar, High performance parallel algorithms for the tucker decomposition of sparse tensors, 45th International Conference on Parallel Processing (ICPP), pp.103-112, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01354894

T. Kolda and B. Bader, The tophits model for higher-order web link analysis, Workshop on Link Analysis, Counterterrorism and Security, 2006.

T. G. Kolda and J. Sun, Scalable tensor decompositions for multi-aspect data mining, Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pp.363-372, 2008.

J. Lieven-de-lathauwer and . Vandewalle, Dimensionality reduction in higher-order signal processing and rank-(r1, r2,...,rn) reduction in multilinear algebra, Linear Algebra and its Applications, vol.391, pp.31-55, 2004.

D. Lee, J. Lee, and H. Yu, Fast tucker factorization for large-scale tensor completion, IEEE International Conference on Data Mining (ICDM), pp.1098-1103, 2018.

X. Li, H. Zhou, and L. Li, Tucker tensor regression and neuroimaging analysis, Statistics in Biosciences, vol.04, p.2013

X. Li, K. Selçuk-candan, and M. L. Sapino, M2td: Multi-task tensor decomposition for sparse ensemble simulations, IEEE 34th International Conference on Data Engineering (ICDE), pp.1144-1155, 2018.

C. Lin, N. Cao, S. Shi-xia-liu, . Papadimitriou, X. Sun et al., Smallblue: Social network analysis for expertise search and collective intelligence, ICDE, pp.1483-1486, 2009.

M. W. Mahoney, M. Maggioni, and P. Drineas, Tensor-cur decompositions for tensor-based data, SIGKDD, pp.327-336, 2006.

C. Navasca and D. N. Pompey, Random projections for low multilinear rank tensors, Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pp.93-106, 2015.

J. Oh, K. Shin, E. E. Papalexakis, C. Faloutsos, and H. Yu, S-hot: Scalable high-order tucker decomposition, Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp.761-770, 2017.

S. Oh, N. Park, L. Sael, and U. Kang, Scalable tucker factorization for sparse tensors -algorithms and discoveries, IEEE 34th International Conference on Data Engineering (ICDE), pp.1120-1131, 2018.

M. Park, J. Jang, and S. Lee, Vest: Very sparse tucker factorization of large-scale tensors, vol.04, 2019.

N. Park, S. Oh, and U. Kang, Fast and scalable method for distributed boolean tensor factorization, The VLDB Journal, pp.1-26, 2019.

I. Perros, R. Chen, R. Vuduc, and J. Sun, Sparse hierarchical tucker factorization and its application to healthcare, pp.943-948, 2015.

K. Shin, L. Sael, and U. Kang, Fully scalable methods for distributed tensor factorization, IEEE Trans. on Knowl. and Data Eng, vol.29, issue.1, pp.100-113, 2017.

N. D. Sidiropoulos, E. E. Papalexakis, and C. Faloutsos, Parallel randomly compressed cubes ( paracomp ) : A scalable distributed architecture for big tensor decomposition, 2014.

S. Smith and G. Karypis, Accelerating the tucker decomposition with compressed sparse tensors, Euro-Par, 2017.

J. Sun, S. Papadimitriou, C. Lin, N. Cao, M. Liu et al., Multivis: Content-based social network exploration through multi-way visual analysis, SDM, 2009.

J. Sun, D. Tao, S. Papadimitriou, P. S. Yu, and C. Faloutsos, Incremental tensor analysis: Theory and applications, vol.2, 2008.

P. Tseng and S. Yun, A coordinate gradient descent method for nonsmooth separable minimization, Mathematical Programming, vol.117, pp.387-423, 2007.

C. E. Tsourakakis, Mach: Fast randomized tensor decompositions, SDM, 2009.

L. R. Tucker, Implications of factor analysis of three-way matrices for measurement of change, Problems in Measuring Change, pp.122-137, 1963.

Y. Xu, On the convergence of higher-order orthogonal iteration. Linear and Multilinear Algebra, pp.2247-2265, 2017.

R. Zdunek, Fast nonnegative matrix factorization algorithms using projected gradient approaches for large-scale problems, Intell. Neuroscience, vol.3, issue.13, pp.1-3, 2008.

Q. Zhao, L. Zhang, and A. Cichocki, Bayesian sparse tucker models for dimension reduction and tensor completion, 2015.

S. Zhe, Y. Qi, Y. Park, Z. Xu, I. Molloy et al., Dintucker: Scaling up gaussian process models on large multidimensional arrays, AAAI, 2016.

S. Zhe, Z. Xu, X. Chu, Y. Qi, and Y. Park, Scalable nonparametric multiway data analysis, AISTATS, 2015.

S. Zhe, K. Zhang, P. Wang, K. Lee, Z. Xu et al., Distributed flexible nonlinear tensor factorization, Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS'16, pp.928-936, 2016.

G. Zhou, Efficient nonnegative tucker decompositions: Algorithms and uniqueness, IEEE Transactions on Image Processing, vol.24, issue.12, pp.4990-5003, 2015.

G. Zhou, A. Cichocki, and S. Xie, Decomposition of big tensors with low multilinear rank, 2014.