Optimization of ℓp-regularized Linear Models via Coordinate Descent
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RIS BIB ENDNOTEOptimization 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.16.005.6186Authors
Optimization of ℓp-regularized Linear Models via Coordinate Descent
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: Schedae Informaticae, 2016, Volume 25, pp. 61 - 72
Article type: Original article
Titles:
Optimization of ℓp-regularized Linear Models via Coordinate Descent
Optimization of ℓp-regularized Linear Models via Coordinate Descent
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
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