Structured sparse support vector machine with ordered features

DOI10.1080/02664763.2020.1849053OpenAlexW3099291533MaRDI QIDQ5073382

Xiaochen Zhang, Peng Wang, Kuangnan Fang, Qing-Zhao Zhang

Publication date: 6 May 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2020.1849053

zbMATH Keywords

variable selectionsupport vector machinelocal oracle propertystructured sparse

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• ElemStatLearn
• gglasso

Cites Work

• Unnamed Item
• Unnamed Item
• Nearly unbiased variable selection under minimax concave penalty
• Statistics for high-dimensional data. Methods, theory and applications.
• One-step sparse estimates in nonconcave penalized likelihood models
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• High-dimensional classification using features annealed independence rules
• Nonconcave penalized likelihood with a diverging number of parameters.
• The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods
• Piecewise linear regularized solution paths
• A finite newton method for classification
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Variable Selection for Support Vector Machines in Moderately High Dimensions
• The elements of statistical learning. Data mining, inference, and prediction
• Gene selection for cancer classification using support vector machines
• A fast unified algorithm for solving group-lasso penalize learning problems

This page was built for publication: Structured sparse support vector machine with ordered features