High-dimensional support union recovery in multivariate regression

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 1217-1224

  Obozinski, Guillaume ^a   Wainwright, Martin J ^a   Jordan, Michael I ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ORTHOGONAL FUNCTIONS; REGRESSION ANALYSIS;

COVARIATES; FIXED SETS; HIGH-DIMENSIONAL; HIGHER-DIMENSIONAL; MULTIVARIATE REGRESSION; OVERLAP FUNCTIONS; REGRESSION PROBLEM; REGULARISATION; SAMPLE SIZES; SCALINGS;

RECOVERY;

EID: 84858774470     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. F. Bach. Consistency of the group Lasso and multiple kernel learning. Technical report, INRIA -Département d'Informatique, Ecole Normale Supérieure, 2008.
2. F. Bach, G. Lanckriet, and M. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In Proc. Int. Conf. Machine Learning (ICML). Morgan Kaufmann, 2004.
3. D. Donoho, M. Elad, and V. M. Temlyakov. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans. Info Theory, 52(1):6-18, January 2006.
4. q regularized regression. Technical Report arXiv:0802.1517v1, Carnegie Mellon University, 2008.
5. L. Meier, S. van de Geer, and P. Bühlmann. The group lasso for logistic regression. Technical report, Mathematics Department, Swiss Federal Institute of Technology Zürich, 2007.
6. Y. Nardi and A. Rinaldo. On the asymptotic properties of the group lasso estimator for linear models. Electronic Journal of Statistics, 2:605-633, 2008.
7. G. Obozinski, B. Taskar, and M. Jordan. Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing, 2009. To appear.
8. M. Pontil and C.A. Michelli. Learning the kernel function via regularization. Journal of Machine Learning Research, 6:1099-1125, 2005.
9. P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman. SpAM: sparse additive models. In Neural Info. Proc. Systems (NIPS) 21, Vancouver, Canada, December 2007.
10. J. A. Tropp. Just relax: Convex programming methods for identifying sparse signals in noise. IEEE Trans. Info Theory, 52(3):1030-1051, March 2006. (Pubitemid 46444890)
11. 1-constrained quadratic programs. Technical Report 709, Department of Statistics, UC Berkeley, 2006.
12. M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society B, 1(68):4967, 2006.
13. P. Zhao, G. Rocha, and B. Yu. Grouped and hierarchical model selection through composite absolute penalties. Technical report, Statistics Department, UC Berkeley, 2007.
14. P. Zhao and B. Yu. Model selection with the lasso. J. of Machine Learning Research, pages 2541-2567, 2007.