Sequential safe feature elimination rule for \(L_1\)-regularized regression with Kullback-Leibler divergence

DOI10.1016/J.NEUNET.2022.09.008MaRDI QIDQ6488733

Hongmei Wang, Yitian Xu, Kun Jiang

Publication date: 18 October 2023

Published in: Neural Networks (Search for Journal in Brave)

zbMATH Keywords

Kullback-Leibler divergencesparse learning\(L_1\)-regularized regressionsafe elimination

Mathematics Subject Classification ID

Statistics (62-XX) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• A safe reinforced feature screening strategy for Lasso based on feasible solutions
• Safe feature screening rules for the regularized Huber regression
• Safe Feature Elimination in Sparse Supervised Learning
• Multiplicative Updates for NMF with $\beta$-Divergences under Disjoint Equality Constraints
• This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice
• Strong Rules for Discarding Predictors in Lasso-Type Problems
• A statistical framework for non-negative matrix factorization based on generalized dual divergence

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