Unit canonical correlations and high-dimensional discriminant analysis

DOI10.1080/00949650903222343zbMath1206.62120OpenAlexW2082270034MaRDI QIDQ3070630

Hyejin Shin, R. L. Eubank

Publication date: 3 February 2011

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949650903222343

zbMATH Keywords

linear discriminant analysishigh-dimension low-sample-size

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items (3)

Recipes for sparse LDA of horizontal data ⋮ Pattern recognition based on canonical correlations in a high dimension low sample size context ⋮ Canonical correlation analysis for irregularly and sparsely observed functional data

Cites Work

• Unnamed Item
• A note on multiple and canonical correlation for a singular covariance matrix
• Generalized inverses. Theory and applications.
• Obtaining squared multiple correlations from a correlation matrix which may be singular
• Geometric Representation of High Dimension, Low Sample Size Data
• The high-dimension, low-sample-size geometric representation holds under mild conditions
• A direct LDA algorithm for high-dimensional data -- with application to face recognition

This page was built for publication: Unit canonical correlations and high-dimensional discriminant analysis