Structured feature selection and task relationship inference for Multi-task Learning

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 171-180

  Fei, Hongliang ^a   Huan, Jun ^a  

^a University of Kansas   (United States)

Author keywords

Multi task learning; Structural sparsity; Structured input and structured output; Task relationship inference

Indexed keywords

EFFICIENT LEARNING; FIRST ORDER; GENERALIZATION PERFORMANCE; HIGH DIMENSIONAL DATA; INPUT FEATURES; ITERATIVE OPTIMIZATION ALGORITHMS; LEARNING METHODS; MODEL PARAMETERS; MTL MODEL; MULTITASK LEARNING; OPTIMIZATION PROBLEMS; PROJECTED GRADIENT; REAL WORLD DATA; STRUCTURAL SPARSITY; STRUCTURE INFORMATION; STRUCTURED INPUT AND STRUCTURED OUTPUT; TASK RELATIONSHIP INFERENCE;

COVARIANCE MATRIX; DATA MINING; GRADIENT METHODS; LEARNING ALGORITHMS; VIRTUAL REALITY;

LINEARIZATION;

EID: 84857149823     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.139     Document Type: Conference Paper

References (39)

1. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. In NIPS, 2006.
2. A. Argyriou, A. Micchelli, M. Pontil, and Y. Ying. A spectral regularization framework for multi-task structure learning. In NIPS, 2007.
3. B. Bakker and T. Heskes. Task clustering and gating for bayesian multitask learning. Journal of Machine Learning Research, 4:2003, 2003.
4. E. V. Bonilla, K. M. Chai, and C. K. I. Williams. Multi-task gaussian process prediction. In NIPS, 2008.
5. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
6. R. Caruana. Multitask learning. Machine Learning, 28(1):41-75, 1997.
7. O. Chapelle, P. K. Shivaswamy, S. Vadrevu, K. Q. Weinberger, Y. Zhang, and B. L. Tseng. Multi-task learning for boosting with application to web search ranking. In KDD, pages 1189-1198, 2010.
8. J. Chen, J. Liu, and J. Ye. Learning incoherent sparse and low-rank patterns from multiple tasks. In The Sixteenth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (SIGKDD 2010), 2010.
9. W. Chen, Y. Yuan, and L. Zhang. Scalable influence maximization in social networks under the linear threshold model. In ICDM, pages 88-97, 2010.
10. X. Chen,W. Pan, J. T. Kwok, and J. G. Carbonell. Accelerated gradient method for multi-task sparse learning problem. IEEE International Conference on Data Mining (ICDM09), 0:746-751, 2009.
11. T. Evgeniou, C. A. Micchelli, and M. Pontil. Learning multiple tasks with kernel methods. Journal of Machine Learning Research, 2005.
12. T. Evgeniou and M. Pontil. Regularized multi-task learning. In KDD, pages 109-117, 2004.
13. H. Fei and J. Huan. Boosting with structure information in the functional space: an application to graph classification. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2010.
14. H. Fei, B. Quanz, and J. Huan. Regularization and feature selection for networked features. In Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM'10), 2010.
15. C. Fellbaum. WordNet: an electronic lexical database. the MIT Press, 1998.
16. V. Iu and S. CL. The phosphatidylinositol 3-kinase akt pathway in human cancer. Nat Rev Cancer., 2(7):489-501, 2002.
17. L. Jacob, F. Bach, and J.-P. Vert. Clustered multi-task learning: a convex formulation. In Advances in Neural Information Processing Systems (NIPS09), 2009.
18. S. Ji and J. Ye. An accelerated gradient method for trace norm minimization. In ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, pages 457-464, New York, NY, USA, 2009. ACM.
19. M. Kanehisa, S. Goto, M. Hattori, K. F. Aoki-Kinoshita, M. Itoh, S. Kawashima, T. Katayama, M. Araki, and M. Hirakawa. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Research, 34:D354-357, 2006.
20. T. Kato, H. Kashima, and K. A. Masashi Sugiyama. Multitask learning via conic programming. In NIPS, 2007.
21. S. Levy. Interactive 3-d visualization of particle systems with partiview. Proceedings of the International Astronomical Union Symposium on "Astrophysical Supercomputing Using Particles, 208, 2001.
22. C. Li and H. Li. Newwork-constrained regularization and variable selection for analysis of genomic data. Bioinformatics, 24(9):1175-1182, 2008.
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 International Conference on Machine Learning (ICML 2009), 2009.
24. 2,1-norm minimization. In Uncertainty in Artificial Intelligence, 2009.
25. T. M. Mitchell, S. V. Shinkareva, A. Carlson, and et al. Predicting human brain activity associated with the meanings of nouns. Science, 320:1191-1195, 2008.
26. Y. Nesterov. Introductory lectures on convex optimization: A basic course. 2003.
27. Y. Nesterov. Gradient methods for minimizing composite objective function. CORE Discussion Paper, 76:265-286, 2007.
28. C. L. Nutt, D. R. Mani, R. A. Betensky, and et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Research, 63:1602-7, 2003/04/01/ 2003.
29. X. Qiu, W. Gao, and X. Huang. Hierarchical multi-label text categorization with global margin maximization. In ACL/AFNLP (Short Papers), pages 165-168, 2009.
30. A. Quattoni, X. Carreras, M. Collins, and T. Darrell. An efficient projection for l1,infinity regularization. In proceedings of The 26th International Conference on Machine Learning (ICML 2009), 2009.
31. D. M. Romero, B. Meeder, and J. M. Kleinberg. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In WWW, pages 695-704, 2011.
32. T. Sandler, P. P. Talukdar, and L. H. Ungar. Regularized learning with networks of features. In NIPS08, 2008.
33. A. Schwaighofer, V. Tresp, and K. Yu. Learning gaussian process kernels via hierarchical bayes. In In Advances in Neural Information Processing Systems (NIPS, pages 1209-1216. MIT Press, 2004.
34. D. Singh, P. G. Febbo, K. Ross, D. G. Jackson, J. Manola, C. Ladd, P. Tamayo, A. A. Renshaw, A. V. D'Amico, J. P. Richie, E. S. Lander, M. Loda, P. W. Kantoff, T. R. Golub, and W. R. Sellers. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 1(2):203-209, 2002.
35. A. I. Su, J. B. Welsh, L. M. Sapinoso, and et al. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Research, 15(20):7388-93, 2001.
36. X. Wang, C. Zhang, and Z. Zhang. Boosted multi-task learning for face verification with applications to web image and video search. In proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009 (CVPR 2009), 2009.
37. J. Yang and J. Leskovec. Modeling information diffusion in implicit networks. In ICDM, pages 599-608, 2010.
38. Y. Zhang and D.-Y. Yeung. A convex formulation for learning task relationships in multi-task learning. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), pages 733-742, 2010.
39. Y. Zhang, D.-Y. Yeung, and Q. Xu. Probabilistic multi-task feature selection. In Proceedings of the Advances in Neural Information Processing Systems (NIPS 2010), pages 2559-2567, 2010.