Independently Interpretable Lasso for Generalized Linear Models

DOI10.1162/neco_a_01279zbMath1473.68171OpenAlexW3019502473WikidataQ94445434 ScholiaQ94445434MaRDI QIDQ5131139

Taiji Suzuki, Masaaki Takada, Hironori Fujisawa

Publication date: 2 November 2020

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

Full work available at URL: https://doi.org/10.1162/neco_a_01279

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

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Uses Software

• glmnet
• sparsenet

Cites Work

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• Pathwise coordinate optimization
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• Just relax: convex programming methods for identifying sparse signals in noise
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
• Regularization and Variable Selection Via the Elastic Net
• Convergence of a block coordinate descent method for nondifferentiable minimization

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