Structured sparse canonical correlation analysis

Journal of Machine Learning Research

Volumn 22, Issue , 2012, Pages 199-207

  Chen, Xi ^a   Liu, Han ^b   Carbonell, Jaime G ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CORRELATION METHODS;

GENOME-WIDE ASSOCIATION STUDIES; OPTIMIZATION ALGORITHMS; OPTIMIZATION TECHNIQUES; PRIMAL-DUAL OPTIMIZATION ALGORITHMS; SPARSE CANONICAL CORRELATION ANALYSIS; STATE-OF-THE-ART METHODS; STRUCTURAL INFORMATION; STRUCTURED SPARSITIES;

OPTIMIZATION;

EID: 84954221126     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (26)

1. Argyriou, A., Evgeniou, T., and Pontil, M. (2008). Convex multi-task feature learning. Machine Learning, 73(3), 243-272.
2. Argyriou, A., Micchelli, C. A., Pontil, M., Shen, L., and Xu, Y. (2011). Efficient first order methods for linear composite regularizers. arXiv:1104.1436v1.
3. Bindea, G., Mlecnik, B., and et. al., H. H. (2009). Cluego: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, 25 (8), 1091-1093.
4. Borwein, J. and Lewis, A. S. (2000). Convex Analysis and Nonlinear Optimization: Theory and Examples. Springer.
5. Chen, X., Lin, Q., Kim, S., Carbonell, J., and Xing, E. (2012). Smoothing proximal gradient method for general structured sparse learning. Annals of Applied Statistics (to be appeared).
6. Duchi, J. and Singer, Y. (2009). Efficient online and batch learning using forward backward splitting. Journal of Machine Learning Research, 10, 2899-2934.
7. Friedman, J., Hastie, T., and Tibshirani, R. (2010). A note on the group lasso and a sparse group lasso.
8. Grant, M. and Boyd, S. (2011). CVX: Matlab software for disciplined convex programming, version 1.21. http://cvxr.com/cvx.
9. Jenatton, R., Audibert, J., and Bach, F. (2009). Structured variable selection with sparsity-inducing norms. Technical report, INRIA.
10. Jenatton, R., Mairal, J., Obozinski, G., and Bach, F. (2010a). Proximal methods for sparse hierarchical dictionary learning. In ICML.
11. Jenatton, R., Obozinski, G., and Bach, F. (2010b). Structured sparse principal component analysis. In AISTATS.
12. Kanehisa, M. and Goto, S. (2000). Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27-30.
13. Kim, S., Sohn, K.-A., and Xing, E. P. (2009). A multivariate regression approach to association analysis of a quantitative trait network. Bioinformatics, 25(12), 204-212.
14. Lee, S., Zhu, J., and Xing, E. P. (2010). Adatpive multi-task lasso: with applications to eqtl detection. In NIPS.
15. Mairal, J., Jenatton, R., Obozinski, G., and Bach, F. (2010). Network flow algorithms for structured sparsity. In NIPS.
16. Nesterov, Y. (2003). Excessive gap technique in non-smooth convex minimization. Technical report, Uni-versit catholique de Louvain, Center for Operations Research and Econometrics (CORE).
17. Nesterov, Y. (2005). Smooth minimization of non-smooth functions. Math Programming, 103(1), 127-152.
18. Qin, Z. and Goldfarb, D. (2011). Structured sparsity via alternating direction methods. Technical report, Columbia University.
19. Shen, X. and Huang, H.-C. (2010). Grouping pursuit through a regularization solution surface. Journal of the American Statistical Association, 105(490), 727-739.
20. Tibshirani, R. and Saunders, M. (2005). Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society: Series B, 67(1), 91-108.
21. Witten, D. and Tibshirani, R. (2009). Extensions of sparse canonical correlation analysis with applications to genomic data. Statistical Applications in Genetics and Molecular Biology, 8 (1), 1-27.
22. Witten, D., Tibshirani, R., and Hastie, T. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics, 10, 515-534.
23. Yuan, L., Liu, J., and Ye, J. (2011). Efficient methods for overlapping group lasso. In NIPS.
24. Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B, 68, 49-67.
25. Zhu, J., Zhang, B., Smith, E. N., Becky Drees4, R. B. B., Kruglyak, L., Bumgarner, R. E., and Schadt, E. E. (2008). Integrating large-scale functional ge-nomic data to dissect the complexity of yeast regulatory networks. Nature Genetics, 40, 854-861.
26. Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B, 67, 301-320.