Sparse common component analysis for multiple high-dimensional datasets via noncentered principal component analysis

DOI10.1007/S00362-018-1045-6zbMath1467.62108OpenAlexW2890684196MaRDI QIDQ2029207

Heewon Park, Sadanori Konishi

Publication date: 3 June 2021

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-018-1045-6

zbMATH Keywords

Gram matrixsparse principal component analysismultiple datasets\(L_1\)-type regularized regressioncommon component analysiscommon feature selection

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Statistical aspects of big data and data science (62R07)

Related Items (1)

Multilinear Common Component Analysis via Kronecker Product Representation

Uses Software

• ElemStatLearn

Cites Work

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• Analysis of a European Union election using principal component analysis
• Centered and non-centered principal component analyses in the frequency domain
• Combining the Liu-type estimator and the principal component regression estimator
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• Regularization and Variable Selection Via the Elastic Net
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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