Using regression makes extraction of shared variation in multiple datasets easy

DOI10.1007/s10618-016-0465-yzbMath1416.62316OpenAlexW2402443440WikidataQ57917570 ScholiaQ57917570MaRDI QIDQ1741273

Jussi Korpela, Andreas Henelius, Lauri Ahonen, Arto Klami, Kai Puolamäki

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-016-0465-y

zbMATH Keywords

multiple regressionregression chainsshared variation

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Measures of association (correlation, canonical correlation, etc.) (62H20) Paired and multiple comparisons; multiple testing (62J15)

Related Items (1)

cocoreg

Uses Software

• R
• sedaR
• e1071
• randomForest
• ElemStatLearn
• CCAGFA

Cites Work

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• Regularized generalized canonical correlation analysis
• Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences
• Understanding Canonical Correlation through the General Linear Model and Principal Components
• A unified approach to multiple‐set canonical correlation analysis and principal components analysis
• Canonical Correlation Analysis: An Overview with Application to Learning Methods
• Canonical analysis of several sets of variables
• RELATIONS BETWEEN TWO SETS OF VARIATES

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