Improving Group Lasso for high-dimensional categorical data. (arXiv:2210.14021v2 [stat.ME] UPDATED)

Sparse modelling or model selection with categorical data is challenging even
for a moderate number of variables, because one parameter is roughly needed to
encode one category or level. The Group Lasso is a well known efficient
algorithm for selection continuous or categorical variables, but all estimates
related to a selected factor usually differ. Therefore, a fitted model may not
be sparse, which makes the model interpretation difficult. To obtain a sparse
solution of the Group Lasso we propose the …

arxiv data lasso