Learning ℓ1-penalized logistic regressions with smooth approximation

The paper presents comparison of learning logistic regression model with different penalty terms. Main part of the paper concerns sparse regression, which includes absolute value function. This function is not strictly convex, thus common optimizers cannot be used directly. In the paper we show that in those cases smooth approximation of absolute value function can be effectively used either in the case of lasso regression or in fussed-lasso like case. One of examples focuses on two dimensional analogue of fussed-lasso model. The experimental results present the comparison of our implementations (in C++ and Python) on three benchmark datasets.