ParCube: Sparse parallelizable tensor decompositions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7523 LNAI, Issue PART 1, 2012, Pages 521-536

  Papalexakis, Evangelos E ^a   Faloutsos, Christos ^a   Sidiropoulos, Nicholas D ^b  

^a Carnegie Mellon University ^*  (United States)

^b University of Minnesota   (United States)

Author keywords

PARAFAC decomposition; parallel algorithms; randomized algorithms; sampling; sparsity; Tensors

Indexed keywords

APPROXIMATION ERRORS; DATA MINING APPLICATIONS; DATA SETS; DECOMPOSITION ALGORITHM; FACEBOOK; LARGE DATA; LARGE DATASETS; MAIN MEMORY; MEMORY REQUIREMENTS; PARAFAC DECOMPOSITION; RANDOMIZED ALGORITHMS; REAL-WORLD DATASETS; SPARSE APPROXIMATIONS; SPARSITY; STATE OF THE ART; TENSOR DECOMPOSITION;

ALGORITHMS; DATA MINING; EXPERIMENTS; LAPLACE TRANSFORMS; LEARNING SYSTEMS; PARALLEL ALGORITHMS; SAMPLING;

TENSORS;

EID: 84866867881     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33460-3_39     Document Type: Conference Paper

References (26)

1. Enron e-mail dataset, http://www.cs.cmu.edu/~enron/
2. Read the web, http://rtw.ml.cmu.edu/rtw/
3. Acar, E., Aykut-Bingol, C., Bingol, H., Bro, R., Yener, B.: Multiway analysis of epilepsy tensors. Bioinformatics 23(13), i10-i18 (2007)
4. Andersson, C.A., Bro, R.: The n-way toolbox for matlab. Chemometrics and Intelligent Laboratory Systems 52(1), 1-4 (2000)
5. Bader, B.W., Kolda, T.G.: Efficient MATLAB computations with sparse and factored tensors. SIAM Journal on Scientific Computing 30(1), 205-231 (2007)
6. Bader, B.W., Berry, M.W., Browne, M.: Discussion tracking in enron email using parafac. Survey of Text Mining II, 147-163 (2008)
7. Bader, B.W., Harshman, R.A., Kolda, T.G.: Temporal analysis of social networks using three-way dedicom. Sandia National Laboratories TR SAND2006-2161 (2006)
8. Bader, B.W., Kolda, T.G.: Matlab tensor toolbox version 2.2. Sandia National Laboratories, Albuquerque (2007)
9. Bro, R.: Parafac. tutorial and applications. Chemometrics and Intelligent Laboratory Systems 38(2), 149-171 (1997)
10. Chew, P.A., Bader, B.W., Kolda, T.G., Abdelali, A.: Cross-language information retrieval using parafac2. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 143-152. ACM (2007)
11. Drineas, P., Kannan, R., Mahoney, M.W., et al.: Fast monte carlo algorithms for matrices iii: Computing a compressed approximate matrix decomposition. SIAM Journal on Computing 36(1), 184 (2006)
12. Harshman, R.A.: Foundations of the parafac procedure: Models and conditions for an "explanatory" multimodal factor analysis (1970)
13. Kolda, T.G., Bader, B.W.: The tophits model for higher-order web link analysis. In: Workshop on Link Analysis, Counterterrorism and Security, vol. 7, pp. 26-29 (2006)
14. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3) (2009)
15. Mahoney, M.W., Maggioni, M., Drineas, P.: Tensor-cur decompositions for tensorbased data. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 327-336. ACM (2006)
16. Maruhashi, K., Guo, F., Faloutsos, C.: Multiaspectforensics: Pattern mining on large-scale heterogeneous networks with tensor analysis. In: Proceedings of the Third International Conference on Advances in Social Network Analysis and Mining (2011)
17. Nion, D., Sidiropoulos, N.D.: Adaptive algorithms to track the parafac decomposition of a third-order tensor. IEEE Transactions on Signal Processing 57(6), 2299-2310 (2009)
18. Pang, R., Allman, M., Bennett, M., Lee, J., Paxson, V., Tierney, B.: A first look at modern enterprise traffic. In: Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, p. 2. USENIX Association (2005)
19. Papalexakis, E.E., Sidiropoulos, N.D.: Co-clustering as multilinear decomposition with sparse latent factors. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2064-2067. IEEE (2011)
20. Phan, A.H., Cichocki, A.: Block decomposition for very large-scale nonnegative tensor factorization. In: 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 316-319. IEEE (2009)
21. Sidiropoulos, N.D., Giannakis, G.B., Bro, R.: Blind parafac receivers for ds-cdma systems. IEEE Transactions on Signal Processing 48(3), 810-823 (2000)
22. Sun, J., Papadimitriou, S., Lin, C.Y., Cao, N., Liu, S., Qian,W.: Multivis: Content-based social network exploration through multi-way visual analysis. In: Proc. SDM, vol. 9, pp. 1063-1074 (2009)
23. Sun, J., Tao, D., Faloutsos, C.: Beyond streams and graphs: dynamic tensor analysis. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 374-383. ACM (2006)
24. Tsourakakis, C.E.: Mach: Fast randomized tensor decompositions, Arxiv preprint arXiv:0909.4969 (2009)
25. Viswanath, B., Mislove, A., Cha, M., Gummadi, K.P.: On the evolution of user interaction in facebook. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Social Networks (WOSN 2009) (August 2009)
26. Zhang, Q., Berry, M.W., Lamb, B.T., Samuel, T.: A Parallel Nonnegative Tensor Factorization Algorithm for Mining Global Climate Data. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009, Part I. LNCS, vol. 5545, pp. 405-415. Springer, Heidelberg (2009)