Distributed Coordinate Descent for L1-regularized Logistic Regression

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Nov 24, 2014
Ilya Trofimov, Alexander Genkin
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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.

* Analysis of Images, Social Networks and Texts. Fourth International Conference, AIST 2015, Yekaterinburg, Russia, April 9-11, 2015, Revised Selected Papers. Communications in Computer and Information Science, Vol. 542, 243-254, Springer  
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