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Distributed Coordinate Descent for L1-regularized Logistic Regression

Authors
  • Trofimov, Ilya
  • Genkin, Alexander
Type
Published Article
Publication Date
Nov 24, 2014
Submission Date
Nov 24, 2014
Identifiers
DOI: 10.1007/978-3-319-26123-2_24
Source
arXiv
License
Yellow
External links

Abstract

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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