High-Dimensional Multivariate Linear Regression with Weighted Nuclear Norm Regularization-Reference-Cited by-同舟云学术

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High-Dimensional Multivariate Linear Regression with Weighted Nuclear Norm Regularization

Published:2024-04-26 Issue: Volume: Page:1-12
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Suh Namjoon¹,Lin Li-Hsiang²,Huo Xiaoming³

Affiliation:

1. Department of Statistics and Data Science, UCLA, Los Angeles, CA

2. Department of Mathematics and Statistics, Georgia State University, Atlanta, GA

3. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA

Funder

National Science Foundation

Georgia Tech

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2024.2331020

Reference31 articles.

1. Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods

2. “Consistency of Trace Norm Minimization,”;Bach F. R.;The Journal of Machine Learning Research,2008

3. Boyd, S., Parikh, N., and Chu, E. (2011), Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Delft: Now Publishers Inc.

4. Chemometrics

5. Optimal selection of reduced rank estimators of high-dimensional matrices

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