An iterative penalized least squares approach to sparse canonical correlation analysis

DOI10.1111/biom.13043zbMath1436.62598OpenAlexW2911502764WikidataQ91325060 ScholiaQ91325060MaRDI QIDQ5214541

Xin Zhang, Qing Mai

Publication date: 7 February 2020

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.13043

zbMATH Keywords

sparsityLASSOcanonical correlation analysis (CCA)penalized least squaresultra-high dimensions

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

Discussion on ‘Review of sparse sufficient dimension reduction’ ⋮ CDPA: common and distinctive pattern analysis between high-dimensional datasets ⋮ Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis ⋮ Generalized Liquid Association Analysis for Multimodal Data Integration ⋮ Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors

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