Influence of grouping in the sparse-group lasso regularization method
In high-dimensional supervised learning problems, sparsity constraints in the solution often lead to better performance and interpretability of the results. For problems in which covariates are grouped and sparse structure are desired, both on group and within group levels, the sparse-group lasso (SGL) regularization method has proved to be very efficient. Sometimes the group structure in the covariates is clear, e.g., when we have dummy variables corresponding to different levels of the same original categorical variable. However, many real problems lack of an explicit configuration for the groups. In this work, we investigate properties of the SGL regularization when the group structure in the covariates is unknown, derive strategies to supply those missing groups and compare the SGL to other methods which do not depend on the choice of groups. We support our analysis using both real and synthetic data sets.
Palabras clave / Keywords: sparse-group lasso high-dimension regularization
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