Feature grouping and sparse principal component analysis with truncated regularization

DOI10.1002/sta4.538MaRDI QIDQ6548777

Oscar-Hernan Madrid-Padilla, Shanshan Qin, Haiyan Jiang

Publication date: 3 June 2024

Published in: Stat (Search for Journal in Brave)

zbMATH Keywords

principal component analysissparsityfeature selectionfeature groupingnon-convex truncated regularization

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Sparse Principal Component Analysis via Variable Projection
• On constrained and regularized high-dimensional regression
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Finite sample approximation results for principal component analysis: A matrix perturbation approach
• The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
• A general truncated regularization framework for contrast-preserving variational signal and image restoration: motivation and implementation
• On constrained optimization with nonconvex regularization
• Joint sparse principal component analysis
• Sparse PCA: optimal rates and adaptive estimation
• On the ``degrees of freedom of the lasso
• Simultaneous supervised clustering and feature selection over a graph
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Joint Group Sparse PCA for Compressed Hyperspectral Imaging
• Sparsity and Smoothness Via the Fused Lasso
• Truncated $L^1$ Regularized Linear Regression: Theory and Algorithm
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• Regularization and Variable Selection Via the Elastic Net

This page was built for publication: Feature grouping and sparse principal component analysis with truncated regularization