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Candecomp / PARAFAC (CP) decomposition, a generalization of the matrix singular value decomposition to higher-dimensional tensors, is a popular tool for analyzing multidimensional sparse data.
H. Kim and H. Park, “Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis,” Bioinformatics , vol. 23, no. 12, pp. 1495–1502, 05 2007
2007
Earlier work this paper cites.
E. Chan, M. Heimlich, A. Purkayastha, and R. van de Geijn, “Collective communication: theory, practice, and experience: Research Articles,” Concurrency and Computation: Practice & Experience , vol. 19, no. 13, pp. 1749–1783, Sep. 2007
2007
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, Aug. 2009, publisher: Society for Industrial and Applied Mathematics
2009
Earlier work this paper cites.
M. W. Mahoney, “Randomized algorithms for matrices and data,” Foundations and Trends® in Machine Learning , vol. 3, no. 2, pp. 123–224, 2011
2011
Earlier work this paper cites.
U. Kang, E. Papalexakis, A. Harpale, and C. Faloutsos, “GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining , ser. KDD ’12. New York, NY, USA: Association for Computing Machinery, Aug. 2012, pp. 316–324
2012
Earlier work this paper cites.
H.-H. Mao, C.-J. Wu, E. E. Papalexakis, C. Faloutsos, K.-C. Lee, and T.-C. Kao, “MalSpot: Multi2 Malicious Network Behavior Patterns Analysis,” in Advances in Knowledge Discovery and Data Mining , ser. Lecture Notes in Computer Science, V. S. Tseng, T. B. Ho, Z.-H. Zhou, A. L. P. Chen, and H.-Y. Kao, Eds. Cham: Springer International Publishing, 2014, pp. 1–14
2014
Earlier work this paper cites.
J. H. Choi and S. Vishwanathan, “DFacTo: Distributed Factorization of Tensors,” in Advances in Neural Information Processing Systems , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger, Eds., vol. 27. Curran Associates, Inc., 2014
2014
Earlier work this paper cites.
E. Solomonik, D. Matthews, J. R. Hammond, J. F. Stanton, and J. Demmel, “A massively parallel tensor contraction framework for coupled-cluster computations,” Journal of Parallel and Distributed Computing , vol. 74, no. 12, pp. 3176–3190, 2014, publisher: Academic Press
2014
Earlier work this paper cites.
H. M. Aktulga, A. Buluç, S. Williams, and C. Yang, “Optimizing sparse matrix-multiple vectors multiplication for nuclear configuration interaction calculations,” in 2014 IEEE 28th International Parallel and Distributed Processing Symposium , 2014, pp. 1213–1222
2014
Earlier work this paper cites.
O. Kaya and B. Uçar, “Scalable sparse tensor decompositions in distributed memory systems,” in SC ’15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , 2015, pp. 1–11
2015
Earlier work this paper cites.
S. Smith and G. Karypis, “Tensor-matrix products with a compressed sparse tensor,” in Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms , ser. IA<sup>3</sup> ’15. New York, NY, USA: Association for Computing Machinery, 2015
2015
Earlier work this paper cites.
N. Park, B. Jeon, J. Lee, and U. Kang, “Bigtensor: Mining billion-scale tensor made easy,” in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management , ser. CIKM ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 2457–2460
2016
Earlier work this paper cites.
D. Cheng, R. Peng, Y. Liu, and I. Perros, “SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling,” in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., vol. 29. Curran Associates, Inc., 2016
2016
Earlier work this paper cites.
P. Drineas and M. W. Mahoney, “RandNLA: Randomized numerical linear algebra,” Commun. ACM , vol. 59, no. 6, p. 80–90, may 2016
2016
Earlier work this paper cites.
S. Smith, J. W. Choi, J. Li, R. Vuduc, J. Park, X. Liu, and G. Karypis, “FROSTT: The Formidable Repository of Open Sparse Tensors and Tools,” 2017. [Online]. Available: http://frostt.io/
2017
Cited alongside, same era.
S. Smith, K. Huang, N. D. Sidiropoulos, and G. Karypis, Streaming Tensor Factorization for Infinite Data Sources . SIAM, 2018, pp. 81–89
2018
Cited alongside, same era.
C. Battaglino, G. Ballard, and T. G. Kolda, “A practical randomized cp tensor decomposition,” SIAM Journal on Matrix Analysis and Applications , vol. 39, no. 2, pp. 876–901, 2018
2018
Cited alongside, same era.
G. Ballard, K. Hayashi, and K. Ramakrishnan, “Parallel nonnegative CP decomposition of dense tensors,” in 2018 IEEE 25th International Conference on High Performance Computing (HiPC) . IEEE, 2018, pp. 22–31
2018
Cited alongside, same era.
J. Li, Y. Ma, and R. Vuduc, “ParTI! : A parallel tensor infrastructure for multicore cpus and gpus,” Oct 2018, last updated: Jan 2020. [Online]. Available: http://parti-project.org
2020
Later among the works it cites.
R. Jin, T. G. Kolda, and R. Ward, “Faster Johnson–Lindenstrauss transforms via Kronecker products,” Information and Inference: A Journal of the IMA , vol. 10, no. 4, pp. 1533–1562, 10 2020
2020
Later among the works it cites.
T. G. Kolda and D. Hong, “Stochastic Gradients for Large-Scale Tensor Decomposition,” SIAM Journal on Mathematics of Data Science , vol. 2, no. 4, pp. 1066–1095, Jan. 2020
2020
Later among the works it cites.
B. W. Larsen and T. G. Kolda, “Practical leverage-based sampling for low-rank tensor decomposition,” SIAM J. Matrix Analysis and Applications , June 2022, accepted for publication
2022
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H. Diao, Z. Song, W. Sun, and D. Woodruff, “Sketching for kronecker product regression and p-splines,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2018, pp. 1299–1308
2018
Cited alongside, same era.
I. Balazevic, C. Allen, and T. Hospedales, “TuckER: Tensor Factorization for Knowledge Graph Completion,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 5185–5194
2019
Cited alongside, same era.
I. Nisa, J. Li, A. Sukumaran-Rajam, P. S. Rawat, S. Krishnamoorthy, and P. Sadayappan, “An efficient mixed-mode representation of sparse tensors,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , ser. SC ’19. New York, NY, USA: Association for Computing Machinery, 2019
2019
Cited alongside, same era.
E. T. Phipps and T. G. Kolda, “Software for sparse tensor decomposition on emerging computing architectures,” SIAM Journal on Scientific Computing , vol. 41, no. 3, pp. C269–C290, 2019
2019
Cited alongside, same era.
T. B. Rolinger, T. A. Simon, and C. D. Krieger, “Performance considerations for scalable parallel tensor decomposition,” Journal of Parallel and Distributed Computing , vol. 129, pp. 83–98, 2019
2019
Cited alongside, same era.
D. Hong, T. G. Kolda, and J. A. Duersch, “Generalized canonical polyadic tensor decomposition,” SIAM Review , vol. 62, no. 1, pp. 133–163, 2020
2020
Cited alongside, same era.
P.-G. Martinsson and J. A. Tropp, “Randomized numerical linear algebra: Foundations and algorithms,” Acta Numerica , vol. 29, p. 403–572, 2020
2020
Cited alongside, same era.
T. D. Ahle, M. Kapralov, J. B. T. Knudsen, R. Pagh, A. Velingker, D. P. Woodruff, and A. Zandieh, “Oblivious sketching of high-degree polynomial kernels,” in Proceedings of the Thirty-First Annual ACM-SIAM Symposium on Discrete Algorithms , ser. SODA ’20. USA: Society for Industrial and Applied Mathematics, 2020, p. 141–160
2020
Cited alongside, same era.
O. A. Malik, “More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees,” in Proceedings of the 39th International Conference on Machine Learning . PMLR, Jun. 2022, pp. 14 887–14 917, iSSN: 2640-3498
2022
Closest in time.
A. Nguyen, A. E. Helal, F. Checconi, J. Laukemann, J. J. Tithi, Y. Soh, T. Ranadive, F. Petrini, and J. W. Choi, “Efficient, out-of-memory sparse mttkrp on massively parallel architectures,” in Proceedings of the 36th ACM International Conference on Supercomputing , ser. ICS ’22. New York, NY, USA: Association for Computing Machinery, 2022
2022
Closest in time.
R. Yadav, A. Aiken, and F. Kjolstad, “Spdistal: Compiling distributed sparse tensor computations,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis , ser. SC ’22. IEEE Press, 2022
2022
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2023
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S. Wijeratne, R. Kannan, and V. Prasanna, “Dynasor: A dynamic memory layout for accelerating sparse mttkrp for tensor decomposition on multi-core cpu,” 2023
2023
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R. Kanakagiri and E. Solomonik, “Minimum cost loop nests for contraction of a sparse tensor with a tensor network,” 2023
2023
Closest in time.
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, iSSN: 1530-2075
2075
Closest in time.
S. Smith and G. Karypis, “A Medium-Grained Algorithm for Sparse Tensor Factorization,” in 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS) , May 2016, pp. 902–911, iSSN: 1530-2075
2075
Closest in time.
L. Ma and E. Solomonik, “Efficient parallel CP decomposition with pairwise perturbation and multi-sweep dimension tree,” in 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) , May 2021, pp. 412–421, iSSN: 1530-2075
2075
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