Group sparsity in nonnegative matrix factorization

Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Volumn , Issue , 2012, Pages 851-862

  Kim, Jingu ^a   Monteiro, Renato D C ^b   Park, Haesun ^a  

^a Georgia Institute of Technology   (United States)

^b Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; CONVEX OPTIMIZATION; DATA MINING; FACTORIZATION;

APPLICATION EXAMPLES; COMPUTATIONAL COMPARISONS; CONVEX OPTIMIZATION METHODS; DATA-MINING TOOLS; MULTILINGUAL TEXTS; NONNEGATIVE MATRIX FACTORIZATION; SCIENCE AND ENGINEERING; SEMI-SUPERVISED CLUSTERING;

MATRIX ALGEBRA;

EID: 84874090367     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972825.73     Document Type: Conference Paper

References (35)

1. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski. Convex optimization with sparsity-inducing norms. In S. Sra, S. Nowozin, and S. J. Wright., editors, Optimization for Machine Learning, pages 19-54. MIT Press, 2011.
2. L. Badea. Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization. In Proceedings of the Pacific Symposium on Biocom- puting 2008, pages 267-278, 2008.
3. S. Bengio, F. Pereira, Y. Singer, and D. Strelow. Group sparse coding. In Advances in Neural Infor- mation Processing Systems 22, pages 82-89. 2009.
4. D. P. Bertsekas. Nonlinear programming. Athena Scientific, 1999.
5. J. M. Borwein and A. S. Lewis. Convex analysis and nonlinear optimization: Theory and Examples. Springer-Verlag, 2006.
6. D. Cai, X. He, and J. Han. Document clustering using locality preserving indexing. IEEE Transactions on Knowledge and Data Engineering, 17(12):1624- 1637, December 2005.
7. X. Chen, W. Pan, J. T. Kwok, and J. G. Carbonell. Accelerated gradient method for multi-task sparse learning problem. In Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, pages 746-751, 2009.
8. 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 Sci- ences, E92-A(3):708-721, 2009.
9. A. Cichocki, R. Zdunek, A. H. Phan, and S.-I. Amari. Nonnegative matrix and tensor factoriza- Tions: Applications to exploratory multi-way data analysis and blind source separation. Wiley, 2009.
10. J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra. Efficient projections onto the l1-ball for learning in high dimensions. In Proceedings of the 25th International Conference on Machine Learn- ing, pages 272-279, 2008.
11. R. Jenatton, J. Mairal, G. Obozinski, and F. Bach. Proximal methods for sparse hierarchical dictionary learning. In Proceedings of the 27th International Conference on Machine Learning, pages 487-494, 2010.
12. R. Jenatton, G. Obozinski, and F. Bach. Structured sparse principal component analysis. In Proceedings of the Thirteenth International Conference on Arti- ficial Intelligence and Statistics (AISTATS), JMLR: W&CP, volume 9, pages 366-373, 2010.
13. Y. Jia, M. Salzmann, and T. Darrell. Factorized latent spaces with structured sparsity. In Advances in Neural Information Processing Systems 23, pages 982-990. 2010.
14. D. Kim, S. Sra, and I. S. Dhillon. Fast Newton-type methods for the least squares nonnegative matrix approximation problem. In Proceedings of the 2007 SIAM International Conference on Data Mining, pages 343-354, 2007.
15. J. Kim and H. Park. Sparse nonnegative matrix factorization for clustering. Technical report, Georgia Institute of Technology Technical Report GT-CSE- 08-01, 2008.
16. J. Kim and H. Park. Toward faster nonnegative matrix factorization: A new algorithm and comparisons. In Proceedings of the 2008 Eighth IEEE In- Ternational Conference on Data Mining, pages 353- 362, 2008.
17. J. Kim and H. Park. Fast nonnegative matrix factorization: An active-set-like method and comparisons. SIAM Journal on Scientific Computing, 33(6):3261- 3281, 2011.
18. S. Kim and E. P. Xing. Tree-guided group Lasso for multi-task regression with structured sparsity. In Proceedings of the 27th International Conference on Machine Learning, pages 543-550, 2010.
19. Y. Kim, J. Kim, and Y. Kim. Blockwise sparse regression. Statistica Sinica, 16(2):375-390, 2006.
20. D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-791, 1999.
21. T. Li, V. Sindhwani, C. Ding, and Y. Zhang. Bridging domains with words: Opinion analysis with matrix tri-factorizations. In Proceedings of the 2010 SIAM International Conference on Data Mining, pages 293-302, 2010.
22. C.-J. Lin. Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19(10):2756-2779, 2007.
23. H. Liu, M. Palatucci, and J. Zhang. Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 649-656, 2009.
24. J. Liu and J. Ye. Moreau-yosida regularization for grouped tree structure learning. In Advances in Neural Information Processing Systems 23, pages 1459-1467. 2010.
25. M. Masaeli, Y. Yan, Y. Cui, G. Fung, and J. G. Dy. Convex principal feature selection. In Proceedings of the 2010 SIAM International Conference on Data Mining, pages 619-628, 2010.
26. L. Meier, S. Van De Geer, and P. Bühlmann. The group lasso for logistic regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(1):53-71, 2008.
27. Y. Nesterov. Introductory lectures on convex opti- mization: A basic course. Kluwer Academic Publishers, 2004.
28. P. M. Pardalos and N. Kovoor. An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds. Mathematical Programming, 46(1-3):321-328, 1990.
29. A. Quattoni, X. Carreras, M. Collins, and T. Darrell. An efficient projection for l1, infty regularization. In Proceedings of the 26th International Con- ference on Machine Learning, pages 857-864, 2009.
30. A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 650-658, 2008.
31. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267-288, 1996.
32. P. Tseng. On accelerated proximal gradient methods for convex-concave optimization. submitted to SIAM Journal on Optimization, 2008.
33. F. Wang, T. Li, and C. Zhang. Semi-supervised clustering via matrix factorization. In Proceedings of the 2008 SIAM International Conference on Data Mining, pages 1-12, 2008.
34. W. Xu, X. Liu, and Y. Gong. Document clustering based on non-negative matrix factorization. In Proceedings of the 26th annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pages 267-273, 2003.
35. M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1):49-67, 2006.