Cross validation in LASSO and its acceleration

DOI10.1088/1742-5468/2016/05/053304zbMath1459.62140arXiv1601.00881OpenAlexW3100953440MaRDI QIDQ3302665

Tomoyuki Obuchi, Yoshiyuki Kabashima

Publication date: 11 August 2020

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1601.00881

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Computational learning theory (68Q32)

Related Items (7)

Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration ⋮ Regularization methods for high-dimensional data as a tool for seafood traceability ⋮ Evaluation of generalized degrees of freedom for sparse estimation by replica method ⋮ A statistical mechanics approach to de-biasing and uncertainty estimation in LASSO for random measurements ⋮ Debiasing the Lasso: optimal sample size for Gaussian designs ⋮ Unnamed Item ⋮ Prediction errors for penalized regressions based on generalized approximate message passing

Uses Software

• ElemStatLearn

Cites Work

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• Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
• A CDMA multiuser detection algorithm on the basis of belief propagation
• Compressed sensing

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