Fetching the paper…
Reading the bibliography…
Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning.
L. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika , vol. 31, no. 3, pp. 279–311, Sep. 1966
1966
Earlier work this paper cites.
R. Harshman, “Foundations of the PARAFAC procedure: Models and conditions for an “explanatory” multimodal factor analysis,” UCLA Working Papers in Phonetics , vol. 16, pp. 1–84, 1970
1970
Earlier work this paper cites.
J. Carroll and J. Chang, “Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition,” Psychometrika , vol. 35, no. 3, pp. 283–319, 1970
1970
Earlier work this paper cites.
——, “Determination and proof of minimum uniqueness conditions for PARAFAC-1,” UCLA Working Papers in Phonetics , vol. 22, pp. 111–117, 1972
1972
Earlier work this paper cites.
R. Bro and N. D. Sidiropoulos, “Least squares regression under unimodality and non-negativity constraints,” Journal of Chemometrics , vol. 12, pp. 223–247, 1998
1998
Earlier work this paper cites.
N. D. Sidiropoulos, R. Bro, and G. Giannakis, “Parallel factor analysis in sensor array processing,” IEEE Transactions on Signal Processing , vol. 48, no. 8, pp. 2377–2388, 2000
2000
Earlier work this paper cites.
N. D. Sidiropoulos, G. Giannakis, and R. Bro, “Blind PARAFAC receivers for DS-CDMA systems,” IEEE Transactions on Signal Processing , vol. 48, no. 3, pp. 810–823, 2000
2000
Earlier work this paper cites.
A. Smilde, R. Bro, P. Geladi, and J. Wiley, Multi-way analysis with applications in the chemical sciences . Wiley, 2004
2004
Earlier work this paper cites.
G. Tomasi and R. Bro, “A comparison of algorithms for fitting the parafac model,” Computational Statistics & Data Analysis , vol. 50, no. 7, pp. 1700–1734, 2006
2006
Earlier work this paper cites.
L. De Lathauwer, “Decompositions of a higher-order tensor in block terms – part ii: Definitions and uniqueness,” SIAM J. Matrix Anal. & Appl. , vol. 30, no. 3, pp. 1033–1066, 2008
2008
Earlier work this paper cites.
J. Dean and S. Ghemawat, “Mapreduce: simplified data processing on large clusters,” Communications of the ACM , vol. 51, no. 1, pp. 107–113, 2008
2008
Earlier work this paper cites.
B. W. Bader and T. G. Kolda, “Efficient MATLAB Computations with Sparse and Factored Tensors,” SIAM Journal on Scientific Computing , vol. 30, no. 1, pp. 205–231, 2008. [Online]. Available: http://dx.doi.org/10.1137/060676489
2008
Cited alongside, same era.
T. G. Kolda and J. Sun, “Scalable tensor decompositions for multi-aspect data mining,” in Proc. IEEE ICDM 2008 , pp. 363–372
2008
Cited alongside, same era.
2009
Cited alongside, same era.
D. Nion, K. Mokios, N. D. Sidiropoulos, and A. Potamianos, “Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 18, no. 6, pp. 1193–1207, 2010
2010
Cited alongside, same era.
B. W. Bader, T. G. Kolda et al. , “Matlab tensor toolbox version 2.5,” Available online, January 2012. [Online]. Available: http://www.sandia.gov/ tgkolda/TensorToolbox/
2012
Later among the works it cites.
L. Sorber, M. Van Barel, and L. De Lathauwer, “Optimization-based algorithms for tensor decompositions: Canonical polyadic decomposition, decomposition in rank- ( l r , l r , 1 ) (l_{r},l_{r},1) terms, and a new generalization,” SIAM Journal on Optimization , vol. 23, no. 2, pp. 695–720, 2013
2013
Later among the works it cites.
A. de Almeida and A. Kibangou, “Distributed computation of tensor decompositions in collaborative networks,” in Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 IEEE 5th International Workshop on , Dec 2013, pp. 232–235
2013
Later among the works it cites.
L. Xu, B. Yu, and Y. Zhang, “An Alternating Direction and Projection Algorithm for Structure-enforced Matrix Factorization,” 2013. [Online]. Available: http://www.caam.rice.edu/ yzhang/reports/tr1311.pdf
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Xu, W. Yin, Z. Wen, and Y. Zhang, “An Alternating Direction Algorithm for Matrix Competion with Nonnegative Factors,” Frontiers of Mathematics in China , vol. 51, no. 2, pp. 365–384, 2010
2010
Cited alongside, same era.
C. Fevotte and A. Ozerov, “Notes on nonnegative tensor factorization of the spectrogram for audio source separation: Statistical insights and towards self-clustering of the spatial cues,” in Exploring Music Contents , ser. Lecture Notes in Computer Science, S. Ystad, M. Aramaki, R. Kronland-Martinet, and K. Jensen, Eds. Springer Berlin, 2011, vol. 6684, pp. 102–115
2011
Cited alongside, same era.
E. Acar, D. M. Dunlavy, and T. G. Kolda, “A scalable optimization approach for fitting canonical tensor decompositions,” Journal of Chemometrics , vol. 25, no. 2, pp. 67–86, 2011. [Online]. Available: http://dx.doi.org/10.1002/cem.1335
2011
Cited alongside, same era.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. Trends Mach. Learn. , vol. 3, no. 1, pp. 1–122, Jan. 2011. [Online]. Available: http://dx.doi.org/10.1561/2200000016
2011
Cited alongside, same era.
R. Gemulla, E. Nijkamp, P. J. Haas, and Y. Sismanis, “Large-scale matrix factorization with distributed stochastic gradient descent,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD ’11. New York, NY, USA: ACM, 2011, pp. 69–77. [Online]. Available: http://doi.acm.org/10.1145/2020408.2020426
2011
Cited alongside, same era.
E. E. Papalexakis, C. Faloutsos, and N. D. Sidiropoulos, “Parcube: Sparse parallelizable tensor decompositions.” in ECML/PKDD (1) , ser. Lecture Notes in Computer Science, P. A. Flach, T. D. Bie, and N. Cristianini, Eds., vol. 7523. Springer, 2012, pp. 521–536
2012
Cited alongside, same era.
U. Kang, E. 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 . ACM, 2012, pp. 316–324
2012
Cited alongside, same era.
Apache, “Hadoop.” [Online]. Available: http://hadoop.apache.org/
Cited in the paper.
2013
Later among the works it cites.
A. Cichocki, D. Mandic, C. Caiafa, A.-H. Phan, G. Zhou, Q. Zhao, and L. De Lathauwer, “Multiway Component Analysis: Tensor Decompositions for Signal Processing Applications,” IEEE Signal Processing Magazine , 2014 (to appear)
2014
Closest in time.
N. D. Sidiropoulos, E. E. Papalexakis, and C. Faloutsos, “A Parallel Algorithm for Big Tensor Decomposition Using Randomly Compressed Cubes (PARACOMP),” in Proc. IEEE ICASSP 2014, May 4-9, Florence, Italy
2014
Closest in time.
——, “Parallel Randomly Compressed Cubes: A Scalable Distributed Architecture for Big Tensor Decomposition,” IEEE Signal Processing Magazine , Sep. 2014
2014
Closest in time.
——, “Distributed large-scale tensor decomposition,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on , May 2014
2014
Closest in time.
N. Ravindran, N. D. Sidiropoulos, S. Smith, and G. Karypis, “Memory-Efficient Parallel Computation of Tensor and Matrix Products for Big Tensor Decomposition,” in Proc. Asilomar Conf. on Signals, Systems, and Computers, Nov. 3-5, 2014
2014
Closest in time.
A. P. Liavas and N. D. Sidiropoulos, “Parallel Algorithms for Constrained Tensor Factorization via the Alternating Direction Method of Multipliers,” Technical Report , 2014
2014
Closest in time.
A. P. Liavas and N. D. Sidiropoulos, “Parallel Algorithms for Large-scale Constrained Tensor Decomposition,” in Proc. IEEE ICASSP 2015, April 19-24, Brisbane, Australia
2015
Closest in time.