Comparison of Feature Selection Methods Based on Lasso

The model and feature selection is one of the important subjects in statistics. Lasso is a feature selection method based on 1-norm. Compared with the existing features of selection methods, Lasso can not only accurately choose the important variables, but also has the stability of feature selection. This paper compares the Lasso algorithm of variable selection in linear regression model, and Lasso, Lars, the Adaptive Lasso, elastic net as well as other methods which are based on the linear model. The relationships between them are presented. A variable selection method is realized by the comparison tests of a few data selected from UCI.