Classification for high-throughput data with an optimal subset of principal components

DOI10.1016/j.compbiolchem.2009.07.017zbMath1403.62211OpenAlexW2044654703WikidataQ33502513 ScholiaQ33502513MaRDI QIDQ1631080

Joon Jin Song, Fenglan Yan, Yuan Ren

Publication date: 5 December 2018

Published in: Computational Biology and Chemistry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compbiolchem.2009.07.017

zbMATH Keywords

classificationcanonical variate analysisprincipal component analysishigh-throughput data

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

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• How many principal components? Stopping rules for determining the number of non-trivial axes revisited
• Optimal classification for time-course gene expression data using functional data analysis
• On the number of principal components: a test of dimensionality based on measurements of similarity between matrices
• Principal component analysis.
• Clustering of time-course gene expression data using functional data analysis
• On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions

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