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Optimization of ℓp-regularized Linear Models via Coordinate Descent

Publication date: 24.03.2017

Schedae Informaticae, 2016, Volume 25, pp. 61 - 72

https://doi.org/10.4467/20838476SI.16.005.6186

Authors

,
Jacek Klimaszewski
Faculty of Computer Science and Information Technology ul. Źołnierska 49, 71-210, Szczecin, Poland
All publications →
Marcin Korzeń
Faculty of Computer Science and Information Technology ul. Źołnierska 49, 71-210, Szczecin, Poland
All publications →

Titles

Optimization of ℓp-regularized Linear Models via Coordinate Descent

Abstract

In this paper we demonstrate, how `p-regularized univariate quadratic loss function can be effectively optimized (for 0 6 p 6 1) without approximation of penalty term and provide analytical solution for p = 1 2 . Next we adapt this approach for important multivariate cases like linear and logistic regressions, using Coordinate Descent algorithm. At the end we compare sample complexity of `1 with `p, 0 6 p < 1 regularized models for artificial and real datasets.

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Information

Information: Schedae Informaticae, 2016, Volume 25, pp. 61 - 72

Article type: Original article

Titles:

Polish:

Optimization of ℓp-regularized Linear Models via Coordinate Descent

English:

Optimization of ℓp-regularized Linear Models via Coordinate Descent

Authors

Faculty of Computer Science and Information Technology ul. Źołnierska 49, 71-210, Szczecin, Poland

Faculty of Computer Science and Information Technology ul. Źołnierska 49, 71-210, Szczecin, Poland

Published at: 24.03.2017

Article status: Open

Licence: None

Percentage share of authors:

Jacek Klimaszewski (Author) - 50%
Marcin Korzeń (Author) - 50%

Article corrections:

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Publication languages:

English