Elastic Net Regularized Logistic Regression using Cubic Majorization
(2014) 22nd International Conference on Pattern Recognition (ICPR 2014) p.3446-3451- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7972402
- author
- Nilsson, Mikael LU
- organization
- publishing date
- 2014
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2014 22nd International Conference on Pattern Recognition (ICPR)
- pages
- 3446 - 3451
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 22nd International Conference on Pattern Recognition (ICPR 2014)
- conference location
- Stockholm, Sweden
- conference dates
- 2014-08-24 - 2014-08-28
- external identifiers
-
- wos:000359818003097
- scopus:84919934120
- ISSN
- 1051-4651
- DOI
- 10.1109/ICPR.2014.593
- language
- English
- LU publication?
- yes
- id
- a4b2e4da-8bef-4cbd-bdbd-3fec275af927 (old id 7972402)
- date added to LUP
- 2016-04-01 14:59:54
- date last changed
- 2022-02-04 23:49:35
@inproceedings{a4b2e4da-8bef-4cbd-bdbd-3fec275af927, 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.}}, author = {{Nilsson, Mikael}}, booktitle = {{2014 22nd International Conference on Pattern Recognition (ICPR)}}, issn = {{1051-4651}}, language = {{eng}}, pages = {{3446--3451}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Elastic Net Regularized Logistic Regression using Cubic Majorization}}, url = {{http://dx.doi.org/10.1109/ICPR.2014.593}}, doi = {{10.1109/ICPR.2014.593}}, year = {{2014}}, }