A New Reduced-Rank Linear Discriminant Analysis Method and Its Applications

DOI10.5705/ss.202015.0387zbMath1382.62034arXiv1511.00282OpenAlexW2962736733MaRDI QIDQ4602121

Bin Dong, Ning Hao, Yue Selena Niu

Publication date: 26 January 2018

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.00282

zbMATH Keywords

dimension reductionhigh-dimensional datagene expression datamulti-class classificationsupervised principal component analysis

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)

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An Efficient Convex Formulation for Reduced-Rank Linear Discriminant Analysis in High Dimensions ⋮ Classification by likelihood accordance functions ⋮ Multiple change-points detection in high dimension ⋮ Sparse quadratic classification rules via linear dimension reduction ⋮ Dynamic linear discriminant analysis in high dimensional space

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