An improved glmnet for L1-regularized logistic regression

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2011, Pages 33-41

  Yuan, Guo Xun ^a   Ho, Chia Hua ^a   Lin, Chih Jen ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Algorithms; Experimentation; Performance

Indexed keywords

ITERATIVE METHODS;

ELASTIC NET; EXPERIMENTATION; GENERALIZED LINEAR MODEL; LARGE-SCALE PROBLEM; LOCAL CONVERGENCE; LOGISTICS REGRESSIONS; NEWTON-TYPE METHODS; OPTIMIZATION PROBLEMS; PERFORMANCE; STATE OF THE ART;

REGRESSION ANALYSIS;

EID: 80052652053     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020408.2020421     Document Type: Conference Paper

References (13)

1. J. Friedman, T. Hastie, and R. Tibshirani, "Regularization paths for generalized linear models via coordinate descent, " Journal of Statistical Software, vol. 33, no. 1, pp. 1-22, 2010.
2. A. Genkin, D. D. Lewis, and D. Madigan, "Large-scale Bayesian logistic regression for text categorization, " Technometrics, vol. 49, no. 3, pp. 291-304, 2007. (Pubitemid 47292487)
3. K. Koh, S.-J. Kim, and S. Boyd, "An interior-point method for large-scale l1-regularized logistic regression, " Journal of Machine Learning Research, vol. 8, pp. 1519-1555, 2007. (Pubitemid 47210273)
4. G. Andrew and J. Gao, "Scalable training of L1-regularized log-linear models, " in Proceedings of the Twenty Fourth International Conference on Machine Learning (ICML), 2007.
5. J. Liu, J. Chen, and J. Ye, "Large-scale sparse logistic regression, " in Proceedings of The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 547-556, 2009.
6. M. Schmidt, G. Fung, and R. Rosales, "Fast optimization methods for l1 regularization: A comparative study and two new approaches, " in Proceedings of European Conference on Machine Learning, pp. 286-297, 2007.
7. G.-X. Yuan, K.-W. Chang, C.-J. Hsieh, and C.-J. Lin, "A comparison of optimization methods and software for large-scale l1-regularized linear classification, " Journal of Machine Learning Research, vol. 11, pp. 3183-3234, 2010.
8. P. Tseng and S. Yun, "A coordinate gradient descent method for nonsmooth separable minimization, " Mathematical Programming, vol. 117, pp. 387-423, 2009.
9. S. Yun and K.-C. Toh, "A coordinate gradient descent method for l1-regularized convex minimization, " Computational Optimizations and Applications, vol. 48, no. 2, pp. 273-307, 2011.
10. K.-W. Chang, C.-J. Hsieh, and C.-J. Lin, "Coordinate descent method for large-scale L2-loss linear SVM, " Journal of Machine Learning Research, vol. 9, pp. 1369-1398, 2008.
11. G.-X. Yuan, C.-H. Ho, and C.-J. Lin, "An improved GLMNET for l1-regularized logistic regression and support vector machines, " tech. rep., National Taiwan University, 2011.
12. H.-F. Yu, H.-Y. Lo, H.-P. Hsieh, J.-K. Lou, T. G. McKenzie, J.-W. Chou, P.-H. Chung, C.-H. Ho, C.-F. Chang, Y.-H. Wei, J.-Y. Weng, E.-S. Yan, C.-W. Chang, T.-T. Kuo, Y.-C. Lo, P. T. Chang, C. Po, C.-Y. Wang, Y.-H. Huang, C.-W. Hung, Y.-X. Ruan, Y.-S. Lin, S.-D. Lin, H.-T. Lin, and C.-J. Lin, "Feature engineering and classifier ensemble for KDD cup 2010, " in JMLR Workshop and Conference Proceedings, 2011. To appear.
13. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, "LIBLINEAR: A library for large linear classification, " Journal of Machine Learning Research, vol. 9, pp. 1871-1874, 2008.