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Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant

arXiv 2405.17094 ↗First posted 27 May 2024stat.ML cs.LGcited by 0

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Abstract (arXiv)

The sparse-group lasso performs both variable and group selection, simultaneously using 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 expensive, due to the added shrinkage complexity, and its additional hyperparameter that needs tuning. This paper presents a novel feature reduction method, Dual Feature Reduction (DFR), that uses strong screening rules for the sparse-group lasso and the adaptive sparse-group lasso to reduce their input space before optimization, without affecting solution optimality. DFR applies two layers of screening through the application of dual norms and subdifferentials. Through synthetic and real data studies, it is shown that DFR drastically reduces the computational cost under many different scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 59 references that are in the map. First posted on arXiv 27 May 2024.

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24 of 59 references.

References · 24

  1. 2019LassoNet: A Neural Network with Feature Sparsitycited by 82
  2. 2016Gap Safe screening rules for sparsity enforcing penaltiescited by 39
  3. 2014SLOPE - Adaptive variable selection via convex optimizationcited by 33
  4. 2016Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modelingcited by 18
  5. 2015GAP Safe screening rules for sparse multi-task and multi-class modelscited by 16
  6. 2013A Safe Screening Rule for Sparse Logistic Regressioncited by 14
  7. 2016Group SLOPE - adaptive selection of groups of predictorscited by 13
  8. 2014Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lassocited by 11
  9. 2016GAP Safe Screening Rules for Sparse-Group-Lassocited by 10
  10. 2020The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimationcited by 9
  11. 2022sparsegl: An R Package for Estimating Sparse Group Lassocited by 9
  12. 2020The Strong Screening Rule for SLOPEcited by 8
12 more
  1. 2014Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Setscited by 7
  2. 2018Adaptive Three Operator Splittingcited by 7
  3. 2023The Contextual Lasso: Sparse Linear Models via Deep Neural Networkscited by 6
  4. 2016Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reductioncited by 5
  5. 2019Adaptive sparse group LASSO in quantile regressioncited by 5
  6. 2023A Survey of Numerical Algorithms that can Solve the Lasso Problemscited by 5
  7. 2023Sparse-group SLOPE: adaptive bi-level selection with FDR-controlcited by 5
  8. 2021The Hessian Screening Rulecited by 3
  9. 2021A scalable hierarchical lasso for gene-environment interactionscited by 2
  10. 2024Strong Screening Rules for Group-based SLOPE Modelscited by 2
  11. 2014Machine Learning Methods in the Computational Biology of Cancercited by 1
  12. 2017Analytic solution and stationary phase approximation for the Bayesian lasso and elastic netcited by 1

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