%0 Journal Article
%T Distributed Coordinate Descent for L1-regularized Logistic Regression
%A Ilya Trofimov
%A Alexander Genkin
%J Computer Science
%D 2014
%I arXiv
%X 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.
%U http://arxiv.org/abs/1411.6520v1