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Robust kanonisk korrelasjonsanalyse (Robust CCA)×Robust diskriminantanalyse×
FagfeltStatistikkStatistikk
FamilieLatent structureRegression model
Opprinnelsesår20031997
OpphavspersonCroux & Dehon (building on Hotelling's CCA framework)Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)
TypeRobust multivariate associationRobust classification / discriminant analysis
Opprinnelig kildeCroux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569. link ↗Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143. DOI ↗
AliasRobust CCA, RCCA, robust CCA, outlier-resistant canonical correlationrobust LDA, high-breakdown discriminant analysis, MCD-based discriminant analysis, Robust Diskriminant Analizi
Relaterte45
SammendragRobust canonical correlation analysis extends classical CCA by replacing the standard sample covariance matrix with a robust estimator — such as the Minimum Covariance Determinant (MCD) or S-estimator — so that outlying observations do not distort the estimated canonical correlations and canonical variates between two sets of variables.Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001).
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ScholarGateSammenlign metoder: Robust Canonical Correlation Analysis · Robust Discriminant Analysis. Hentet 2026-06-17 fra https://scholargate.app/no/compare