%0 Journal Article
%T Optimization of ℓp-regularized Linear Models via Coordinate Descent
%A Klimaszewski, Jacek
%A Korzeń, Marcin
%J Schedae Informaticae
%V 2016
%R 10.4467/20838476SI.16.005.6186
%N Volume 25
%P 61-72
%K Classification, Coordinate Descent, Regression, Sparsity
%@ 1732-3916
%D 2017
%U https://ejournals.eu/en/journal/schedae-informaticae/article/optimization-of-p-regularized-linear-models-via-coordinate-descent
%X 
	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.