Smoothing proximal gradient method for general structured sparse learning

Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

Volumn , Issue , 2011, Pages 105-114

  Chen, Xi ^a   Lin, Qihang ^b   Kim, Seyoung ^a   Carbonell, Jaime G ^a   Xing, Eric P ^a  

^a Carnegie Mellon University   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GRADIENT METHODS; NUMERICAL METHODS; REGRESSION ANALYSIS;

GENERAL OPTIMIZATIONS; HIGH-DIMENSIONAL REGRESSIONS; INTERIOR-POINT METHOD; OPTIMIZATION METHOD; SMOOTHING TECHNIQUES; STRUCTURAL INFORMATION; STRUCTURED SPARSITIES; SUB-GRADIENT METHODS;

LEARNING SYSTEMS;

EID: 80053139009     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. A. Argyriou, T. Evgeniou, and M. Pontil. Convex multi-task feature learning. Machine Learning, 73:243-272, 2008.
2. A. Beck and M. Teboulle. A fast iterative shrinkage thresholding algorithm for linear inverse problems. SIAM Journal of Image Science, 2(1):183-202, 2009.
3. D. Bertsekas. Nonlinear Programming. Athena Scientific, 1999.
4. J. Duchi and Y. Singer. Efficient online and batch learning using forward backward splitting. Journal of Machine Learning Research, 10:2899-2934, 2009.
5. J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani. Pathwise coordinate optimization. Annals of Applied Statistics, 1:302-332, 2007.
6. R. Jenatton, J. Y. Audibert, and F. Bach. Structured variable selection with sparsity-inducing norms. Technical report, INRIA, 2009.
7. R. Jenatton, J. Mairal, G. Obozinski, and F. Bach. Proximal methods for sparse hierarchical dictionary learning. In ICML, 2010.
8. S. Kim, K. A. Sohn, and E. P. Xing. A multivariate regression approach to association analysis of a quantitative trait network. Bioinformatics, 25(12):204-212, 2009.
9. S. Kim and E. P. Xing. Tree-guided group lasso for multi-task regression with structured sparsity. In ICML, 2010.
10. G. Lan, Z. Lu, and R. Monteiro. Primal-dual first-order methods with O(1=ε) iteration complexity for cone programming. Mathematical Programming, 126:1-29, 2011.
11. 2;1-norm minimization. In UAI, 2009.
12. J. Liu and J. Ye. Fast overlapping group lasso. ArXiv:1009.0306v1[cs.LG].
13. J. Liu and J. Ye. Moreau-yosida regularization for grouped tree structure learning. In NIPS, 2010.
14. J. Liu, L. Yuan, and J. Ye. An efficient algorithm for a class of fused lasso problems. In ACM SIGKDD, 2010.
15. J. Mairal, R. Jenatton, G. Obozinski, and F. Bach. Network flow algorithms for structured spasity. In NIPS, 2010.
16. Y. Nesterov. Gradient methods for minimizing composite objective function. ECORE Discussion Paper 2007.
17. Y. Nesterov. Smooth minimization of non- smooth functions. Mathematical Programming, 103(1):127-152, 2005.
18. G. Obozinski, B. Taskar, and M. I. Jordan. High- dimensional union support recovery in multivari- ate regression. In NIPS, 2009.
19. R. Rockafellar. Convex Analysis. Princeton Univ. Press, 1996.
20. The International HapMap Consortium. A hap- lotype map of the human genome. Nature, 437:1399-1320, 2005.
21. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical So- ciety: Series B, 58:267-288, 1996.
22. R. Tibshirani and M. Saunders. Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society, Series B, 67(1):91-108, 2005. (Pubitemid 40167043)
23. R. H. Tütüncü, K. C. Toh, and M. J. Todd. Solving semidefinite-quadratic-linear programs using sdpt3. Mathematical Programming, 95:189-217, 2003. (Pubitemid 44757853)
24. M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B, 68:49-67, 2006. (Pubitemid 43415335)
25. P. Zhao, G. Rocha, and B. Yu. The composite absolute penalties family for grouped and hierarchical variable selection. The Annals of Statistics, 37(6A):3468-3497, 2009.