Comparison of Optimization Methods for L1-regularized Logistic Regression.
Aleksander Jovanovich, Alina Lazar · 2012
Logistic regression with L1-regularization has been recognized as a prominent method for feature extraction in linear classification problems. Various optimization methods for L1 logistic regression have been proposed in recent years. However there have been few studies conducted to compare such methods. This paper reviews existing methods for optimization and then tests the methods over a binary dataset. Results are recorded and comparisons are made. After analyzing the results, the conclusion is that the GLMNET method is the best in terms of time efficiency.