Regularized Linear Programming Discriminant Rule with Folded Concave Penalty for Ultrahigh-Dimensional Data-Reference-Cited by-同舟云学术

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

Published:2022-12-01 Issue: Volume: Page:1-9
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Li Changcheng¹^ORCID,Li Runze²^ORCID,Wen Jiawei³,Yang Songshan⁴,Zhan Xiang⁵^ORCID

Affiliation:

1. School of Mathematical Sciences, Dalian University of Technology, Dalian, China

2. Department of Statistics, The Pennsylvania State University, University Park, PA

3. Meta, Menlo Park, CA

4. The Center for Applied Statistics and Institute of Statistics and Big Data, Renmin University of China, Beijing, China

5. Department of Biostatistics, School of Public Health and Beijing International Center for Mathematical Research, Peking University, Beijing, China

Funder

NSF

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2022.2143785

Reference22 articles.

1. Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations

2. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

3. A Direct Estimation Approach to Sparse Linear Discriminant Analysis

4. A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation

5. High-dimensional classification using features annealed independence rules

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