Distributed Coordinate Descent for L1-regularized Logistic Regression

@inproceedings{Trofimov2015DistributedCD,
  title={Distributed Coordinate Descent for L1-regularized Logistic Regression},
  author={Ilya Trofimov and Alexander Genkin},
  booktitle={AIST},
  year={2015}
}
Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memory of a single machine. We present d-GLMNET, a new algorithm solving logistic regression with L1-regularization in the distributed settings. We empirically show that it is superior over distributed online learning via truncated gradient. 
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