Simultaneous feature selection and clustering based on square root optimization

DOI10.1016/j.ejor.2020.06.045zbMath1487.62086OpenAlexW3039543396MaRDI QIDQ2028812

Yao Dong, Shi Hua Luo, He Jiang

Publication date: 3 June 2021

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2020.06.045

zbMATH Keywords

clusteringalternating direction method of multipliersfeature selectionanalyticssquare root fused Lasso

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of mathematical programming (90C90)

Related Items (3)

Preference estimation under bounded rationality: identification of attribute non-attendance in stated-choice data using a support vector machines approach ⋮ Sparse and robust estimation with ridge minimax concave penalty ⋮ Dendrograms, minimum spanning trees and feature selection

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• The Adaptive Lasso and Its Oracle Properties
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• A stepwise regression method and consistent model selection for high-dimensional sparse linear models
• Feature selection for support vector machines using generalized Benders decomposition
• Oracle inequalities and optimal inference under group sparsity
• Properties and refinements of the fused Lasso
• Estimating the dimension of a model
• On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
• Sparse spatial autoregressions
• High dimensional data classification and feature selection using support vector machines
• Least angle regression. (With discussion)
• The risk inflation criterion for multiple regression
• On the conditions used to prove oracle results for the Lasso
• Simultaneous analysis of Lasso and Dantzig selector
• Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators
• Pathwise coordinate optimization
• High-dimensional graphs and variable selection with the Lasso
• Forward Regression for Ultra-High Dimensional Variable Screening
• Square-root lasso: pivotal recovery of sparse signals via conic programming
• Extended Bayesian information criteria for model selection with large model spaces
• Spatial smoothing and hot spot detection for CGH data using the fused lasso
• Bayes Model Averaging with Selection of Regressors
• Sparsity and Smoothness Via the Fused Lasso
• Statistics for high-dimensional data
• Regularization and Variable Selection Via the Elastic Net
• The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms
• Model Selection and Estimation in Regression with Grouped Variables
• Some Comments on C P
• A new look at the statistical model identification
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Simultaneous feature selection and clustering based on square root optimization