2011 AnImprovedGLMNETforL1Regularize

• (Yuan et al., 2011) ⇒ Guo-Xun Yuan, Chia-Hua Ho, and Chih-Jen Lin. (2011). “An Improved GLMNET for L1-regularized Logistic Regression.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020421

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+An+Improved+GLMNET+for+L1-regularized+Logistic+Regression
• http://dl.acm.org/citation.cfm?id=2020408.2020421&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; classifier design and evaluation; experimentation; l1 regularization; linear classification; logistic regression; performance

Abstract

GLMNET proposed by Friedman et al. is an algorithm for generalized linear models with elastic net. It has been widely applied to solve L1-regularized logistic regression. However, recent experiments indicated that the existing GLMNET implementation may not be stable for large-scale problems. In this paper, we propose an improved GLMNET to address some theoretical and implementation issues. In particular, as a Newton-type method, GLMNET achieves fast local convergence, but may fail to quickly obtain a useful solution. By a careful design to adjust the effort for each iteration, our method is efficient regardless of loosely or strictly solving the optimization problem. Experiments demonstrate that the improved GLMNET is more efficient than a state-of-the-art coordinate descent method.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 AnImprovedGLMNETforL1Regularize	Chih-Jen Lin Guo-Xun Yuan Chia-Hua Ho			An Improved GLMNET for L1-regularized Logistic Regression				10.1145/2020408.2020421		2011