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The CP tensor decomposition is a low-rank approximation of a tensor.
P. Paatero, “A weighted non-negative least squares algorithm for three-way PARAFAC factor analysis,” Chemometrics and Intelligent Laboratory Systems , vol. 38, no. 2, pp. 223 – 242, 1997. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0169743997000312
1997
Earlier work this paper cites.
D. P. Bertsekas, Nonlinear Programming . Belmont, MA: Athena Scientific, 1999
1999
Earlier work this paper cites.
D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature , vol. 401, no. 6755, p. 788, 1999
1999
Earlier work this paper cites.
M. Welling and M. Weber, “Positive tensor factorization,” Pattern Recognition Letters , vol. 22, no. 12, pp. 1255 – 1261, 2001, selected Papers from the 11th Portuguese Conference on Pattern Recognition. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0167865501000708
2001
Earlier work this paper cites.
R. Thakur, R. Rabenseifner, and W. Gropp, “Optimization of collective communication operations in MPICH,” International Journal of High Performance Computing Applications , vol. 19, no. 1, pp. 49–66, 2005. [Online]. Available: http://hpc.sagepub.com/content/19/1/49.abstract
2005
Earlier work this paper cites.
E. Chan, M. Heimlich, A. Purkayastha, and R. van de Geijn, “Collective communication: theory, practice, and experience,” Concurrency and Computation: Practice and Experience , vol. 19, no. 13, pp. 1749–1783, 2007. [Online]. Available: http://dx.doi.org/10.1002/cpe.1206
2007
Earlier work this paper cites.
A. Cichocki and A.-H. Phan, “Fast local algorithms for large scale nonnegative matrix and tensor factorizations,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences , vol. E92-A, pp. 708–721, 2009
2009
Earlier work this paper cites.
T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM review , vol. 51, no. 3, pp. 455–500, 2009
2009
Earlier work this paper cites.
T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Review , vol. 51, no. 3, pp. 455–500, September 2009. [Online]. Available: http://epubs.siam.org/doi/abs/10.1137/07070111X
2009
Earlier work this paper cites.
C. Sanderson, “Armadillo: An open source C++ linear algebra library for fast prototyping and computationally intensive experiments,” NICTA, Tech. Rep., 2010. [Online]. Available: http://arma.sourceforge.net/armadillo_nicta_2010.pdf
2010
Earlier work this paper cites.
G. Guennebaud, B. Jacob et al. , “Eigen v3,” http://eigen.tuxfamily.org, 2010
2010
Earlier work this paper cites.
J. Kim and H. Park, “Fast nonnegative matrix factorization: An active-set-like method and comparisons,” SIAM Journal on Scientific Computing , vol. 33, no. 6, pp. 3261–3281, 2011
2011
Earlier work this paper cites.
A. H. Phan and A. Cichocki, “PARAFAC algorithms for large-scale problems,” Neurocomputing , vol. 74, no. 11, pp. 1970–1984, 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0925231211000415
2011
Cited alongside, same era.
D. V. Essen, K. Ugurbil, E. Auerbach, D. Barch, T. Behrens, R. Bucholz, A. Chang, L. Chen, M. Corbetta, S. Curtiss, S. D. Penna, D. Feinberg, M. Glasser, N. Harel, A. Heath, L. Larson-Prior, D. Marcus, G. Michalareas, S. Moeller, R. Oostenveld, S. Petersen, F. Prior, B. Schlaggar, S. Smith, A. Snyder, J. Xu, and E. Yacoub, “The Human Connectome Project: a data acquisition perspective,” Neuroimage , vol. 62, no. 4, pp. 2222–2231, 2012
2012
Cited alongside, same era.
A.-H. Phan, P. Tichavsky, and A. Cichocki, “Fast alternating LS algorithms for high order CANDECOMP/PARAFAC tensor factorizations,” IEEE Transactions on Signal Processing , vol. 61, no. 19, pp. 4834–4846, Oct 2013
2013
Cited alongside, same era.
R. Kannan, G. Ballard, and H. Park, “A high-performance parallel algorithm for nonnegative matrix factorization,” in Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming , ser. PPoPP ’16. New York, NY, USA: ACM, February 2016, pp. 9:1–9:11. [Online]. Available: http://doi.acm.org/10.1145/2851141.2851152
2016
Later among the works it cites.
G. Ballard, N. Knight, and K. Rouse, “Communication lower bounds for matricized tensor times Khatri-Rao product,” arXiv, Tech. Rep. 1708.07401, 2017
2017
Later among the works it cites.
A. P. Liavas, G. Kostoulas, G. Lourakis, K. Huang, and N. D. Sidiropoulos, “Nesterov-based alternating optimization for nonnegative tensor factorization: Algorithm and parallel implementation,” IEEE Transactions on Signal Processing , Nov 2017. [Online]. Available: http://ieeexplore.ieee.org/document/8119874/
2017
Later among the works it cites.
S. Smith, A. Beri, and G. Karypis, “Constrained tensor factorization with accelerated AO-ADMM,” in 2017 46th International Conference on Parallel Processing (ICPP) , Aug 2017, pp. 111–120
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2013
Cited alongside, same era.
J. Kim, Y. He, and H. Park, “Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework,” Journal of Global Optimization , vol. 58, no. 2, pp. 285–319, Feb 2014. [Online]. Available: https://doi.org/10.1007/s10898-013-0035-4
2014
Cited alongside, same era.
K. Huang, N. D. Sidiropoulos, and A. P. Liavas, “Efficient algorithms for universally constrained matrix and tensor factorization,” in Signal Processing Conference (EUSIPCO), 2015 23rd European . IEEE, 2015, pp. 2521–2525
2015
Cited alongside, same era.
S. Smith, N. Ravindran, N. D. Sidiropoulos, and G. Karypis, “SPLATT: Efficient and parallel sparse tensor-matrix multiplication,” in 2015 IEEE International Parallel and Distributed Processing Symposium , May 2015, pp. 61–70
2015
Cited alongside, same era.
S. Jesse, M. Chi, A. Borisevich, A. Belianinov, S. Kalinin, E. Endeve, R. K. Archibald, C. T. Symons, and A. R. Lupini, “Using multivariate analysis of scanning-Ronchigram data to reveal material functionality,” Microscopy and Microanalysis , vol. 22, pp. 292–293, 07 2016
2016
Cited alongside, same era.
D. H. Foster, K. Amano, and S. M. Nascimento, “Time-lapse ratios of cone excitations in natural scenes,” Vision Research , vol. 120, pp. 45 – 60, 2016, vision and the Statistics of the Natural Environment. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0042698915001121
2016
Cited alongside, same era.
O. Kaya and B. Uçar, “High performance parallel algorithms for the tucker decomposition of sparse tensors,” in 45th International Conference on Parallel Processing, ICPP 2016, Philadelphia, PA, USA, August 16-19, 2016 , 2016, pp. 103–112. [Online]. Available: https://doi.org/10.1109/ICPP.2016.19
2016
Cited alongside, same era.
S. Smith and G. Karypis, “A medium-grained algorithm for distributed sparse tensor factorization,” in IEEE 30th International Parallel and Distributed Processing Symposium , May 2016, pp. 902–911
2016
Cited alongside, same era.
O. Kaya and B. Uçar, “Parallel CP decomposition of sparse tensors using dimension trees,” Inria - Research Centre Grenoble – Rhône-Alpes, Research Report RR-8976, Nov. 2016. [Online]. Available: https://hal.inria.fr/hal-01397464
2016
Cited alongside, same era.
2017
Later among the works it cites.
A. P. Liavas, G. Kostoulas, G. Lourakis, K. Huang, and N. D. Sidiropoulos, “Nesterov-based parallel algorithm for large-scale nonnegative tensor factorization,” in Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on . IEEE, 2017, pp. 5895–5899
2017
Later among the works it cites.
N. D. Sidiropoulos, L. D. Lathauwer, X. Fu, K. Huang, E. E. Papalexakis, and C. Faloutsos, “Tensor decomposition for signal processing and machine learning,” IEEE Transactions on Signal Processing , vol. 65, no. 13, pp. 3551–3582, July 2017
2017
Later among the works it cites.
J. Li, J. Choi, I. Perros, J. Sun, and R. Vuduc, “Model-driven sparse CP decomposition for higher-order tensors,” in IEEE International Parallel and Distributed Processing Symposium , ser. IPDPS, May 2017, pp. 1048–1057
2017
Later among the works it cites.
O. Kaya, “High performance parallel algorithms for tensor decompositions,” Ph.D. dissertation, University of Lyon, Sep. 2017. [Online]. Available: https://tel.archives-ouvertes.fr/tel-01623523
2017
Later among the works it cites.
M. J. Tobia, K. Hayashi, G. Ballard, I. H. Gotlib, and C. E. Waugh, “Dynamic functional connectivity and individual differences in emotions during social stress,” Human Brain Mapping , vol. 38, no. 12, pp. 6185–6205, 2017. [Online]. Available: http://dx.doi.org/10.1002/hbm.23821
2017
Later among the works it cites.
R. Kannan, G. Ballard, and H. Park, “MPI-FAUN: An MPI-based framework for alternating-updating nonnegative matrix factorization,” IEEE Transactions on Knowledge and Data Engineering , vol. 30, no. 3, pp. 544–558, 2018
2018
Closest in time.
O. Kaya and B. Uçar, “Parallel CANDECOMP/PARAFAC decomposition of sparse tensors using dimension trees,” SIAM J. Scientific Computing , vol. 40, no. 1, 2018. [Online]. Available: https://doi.org/10.1137/16M1102744
2018
Closest in time.
K. Hayashi, G. Ballard, Y. Jiang, and M. J. Tobia, “Shared-memory parallelization of MTTKRP for dense tensors,” in Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming , ser. PPoPP ’18. New York, NY, USA: ACM, 2018, pp. 393–394. [Online]. Available: http://doi.acm.org/10.1145/3178487.3178522
2018
Closest in time.