Sparse principal component regression via singular value decomposition approach

DOI10.1007/s11634-020-00435-2OpenAlexW3127190032MaRDI QIDQ2051586

Shuichi Kawano

Publication date: 24 November 2021

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.09188

zbMATH Keywords

ADMMsingular value decompositionprincipal component analysislassoone-stage procedure

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items

Sparse dimension reduction based on energy and ball statistics

Uses Software

• R
• hglasso
• PMA
• rare

Cites Work

• Unnamed Item
• Unnamed Item
• A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nearly unbiased variable selection under minimax concave penalty
• Rare Feature Selection in High Dimensions
• Sparse Principal Component Analysis via Variable Projection
• The Adaptive Lasso and Its Oracle Properties
• A penalized maximum likelihood approach to sparse factor analysis
• Kernel ridge vs. principal component regression: minimax bounds and the qualification of regularization operators
• Sparse logistic principal components analysis for binary data
• Split Bregman method for large scale fused Lasso
• Sparse principal component analysis via regularized low rank matrix approximation
• Linearized alternating direction method of multipliers for sparse group and fused Lasso models
• Sparse principal component regression for generalized linear models
• Sparse principal component regression with adaptive loading
• Principal component analysis.
• Least angle regression. (With discussion)
• Combining two-parameter and principal component regression estimators
• Rotation to simple loadings using component loss functions: the oblique case
• Pathwise coordinate optimization
• Coordinate descent algorithms for lasso penalized regression
• Sparse Convex Clustering
• Automatic Response Category Combination in Multinomial Logistic Regression
• Functional Principal Component Regression and Functional Partial Least Squares
• Ideal spatial adaptation by wavelet shrinkage
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
• A Statistical View of Some Chemometrics Regression Tools
• The Linearized Alternating Direction Method of Multipliers for Dantzig Selector
• The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
• Regularization and Variable Selection Via the Elastic Net
• A Direct Formulation for Sparse PCA Using Semidefinite Programming
• Kernel PCA for feature extraction and de-noising in nonlinear regression
• Functional Principal Component Regression and Functional Partial Least‐squares Regression: An Overview and a Comparative Study