An improved GLMNET for l1-regularized logistic regression

@inproceedings{Yuan2011AnIG,
  title={An improved GLMNET for l1-regularized logistic regression},
  author={Guo-Xun Yuan and Chia-Hua Ho and Chih-Jen Lin},
  booktitle={KDD},
  year={2011}
}
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
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