An improved GLMNET for l1-regularized logistic regression

  title={An improved GLMNET for l1-regularized logistic regression},
  author={Guo-Xun Yuan and Chia-Hua Ho and Chih-Jen Lin},
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… CONTINUE READING
Highly Influential
This paper has highly influenced 19 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 176 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 103 extracted citations

177 Citations

Citations per Year
Semantic Scholar estimates that this publication has 177 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.

Similar Papers

Loading similar papers…