Regularized Linear Programming Discriminant Rule with Folded Concave Penalty for Ultrahigh-Dimensional Data

DOI10.1080/10618600.2022.2143785MaRDI QIDQ6180737

Unnamed Author, Changcheng Li, Run-Ze Li, Song-Shan Yang, Xiang Zhan

Publication date: 22 January 2024

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://figshare.com/articles/journal_contribution/Regularized_Linear_Programming_Discriminant_Rule_with_Folded_Concave_Penalty_for_Ultrahigh-Dimensional_Data/21657762

zbMATH Keywords

ADMMfolded concave penaltyfeature-splitlinear programming discriminant

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nearly unbiased variable selection under minimax concave penalty
• Sparse linear discriminant analysis by thresholding for high dimensional data
• One-step sparse estimates in nonconcave penalized likelihood models
• High-dimensional classification using features annealed independence rules
• Folded concave penalized sparse linear regression: sparsity, statistical performance, and algorithmic theory for local solutions
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• Strong oracle optimality of folded concave penalized estimation
• Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• A Direct Estimation Approach to Sparse Linear Discriminant Analysis
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Statistical Foundations of Data Science
• A Convergent 3-Block SemiProximal Alternating Direction Method of Multipliers for Conic Programming with 4-Type Constraints