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Elastic Net Regularized Logistic Regression using Cubic Majorization

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Author

Summary, in English

In this work, a coordinate solver for elastic net regularized logistic regression is proposed. In particular, a method based on majorization maximization using a cubic function is derived. This to reliably and accurately optimize the objective function at each step without resorting to line search. Experiments show that the proposed solver is comparable to, or improves, state-of-the-art solvers. The proposed method is simpler, in the sense that there is no need for any line search, and can directly be used for small to large scale learning problems with elastic net regularization.

Publishing year

2014

Language

English

Pages

3446-3451

Publication/Series

2014 22nd International Conference on Pattern Recognition (ICPR)

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Computational Mathematics

Conference name

22nd International Conference on Pattern Recognition (ICPR 2014)

Conference date

2014-08-24 - 2014-08-28

Conference place

Stockholm, Sweden

Status

Published

ISBN/ISSN/Other

  • ISSN: 1051-4651