Elastic Net Regularized Logistic Regression using Cubic Majorization

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Abstract

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.
Original languageEnglish
Title of host publication2014 22nd International Conference on Pattern Recognition (ICPR)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages3446-3451
DOIs
Publication statusPublished - 2014
Event22nd International Conference on Pattern Recognition (ICPR 2014) - Stockholm, Sweden
Duration: 2014 Aug 242014 Aug 28
Conference number: 22

Publication series

Name
ISSN (Print)1051-4651

Conference

Conference22nd International Conference on Pattern Recognition (ICPR 2014)
Country/TerritorySweden
CityStockholm
Period2014/08/242014/08/28

Subject classification (UKÄ)

  • Computational Mathematics

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