Dual feature reduction for the sparse-group lasso and its adaptive variant
<|reference_start|>Dual feature reduction for the sparse-group lasso and its adaptive variant: The sparse-group lasso performs both variable and group selection, making simultaneous use of the strengths of the lasso and group lasso. It has found widespread use in genetics, a field that regularly involves the analysis of high-dimensional data, due to its sparse-group penalty, which allows it to utilize grouping information. However, the sparse-group lasso can be computationally more expensive than both the lasso and group lasso, due to the added shrinkage complexity, and its additional hyper-parameter that needs tuning. In this paper a novel dual feature reduction method, Dual Feature Reduction (DFR), is presented that uses strong screening rules for the sparse-group lasso and the adaptive sparse-group lasso to reduce their input space before optimization. DFR applies two layers of screening and is based on the dual norms of the sparse-group lasso and adaptive sparse-group lasso. Through synthetic and real numerical studies, it is shown that the proposed feature reduction approach is able to drastically reduce the computational cost in many different scenarios.<|reference_end|>
@article{feser2024dual,
    title={Dual feature reduction for the sparse-group lasso and its adaptive
  variant},
    author={Fabio Feser, Marina Evangelou},
    journal={arXiv preprint arXiv:2405.17094},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17094},
    primaryClass={stat.ML cs.LG}
}