เปรียบเทียบวิธี

	Robust Canonical Correlation Analysis	Canonical Correlation Analysis
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Latent structure	Latent structure
ปีกำเนิด	2003	1936
ผู้ริเริ่ม	Croux & Dehon (building on Hotelling's CCA framework)	Harold Hotelling
ประเภท	Robust multivariate association	Multivariate linear dimension reduction and association
แหล่งต้นตำรับ	Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569.	Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377.
ชื่อเรียกอื่น	Robust CCA, RCCA, robust CCA, outlier-resistant canonical correlation	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation
ที่เกี่ยวข้อง	4	4
สรุป	Robust 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.	Canonical Correlation Analysis (CCA) is a multivariate statistical method that identifies pairs of linear combinations — one from each of two variable sets — such that the correlation between each pair is maximised. Introduced by Harold Hotelling in his landmark 1936 Biometrika paper, CCA provides the most general linear framework for studying the association between two multivariate batteries of measurements, and many classical procedures (multiple regression, MANOVA, discriminant analysis) are special cases of it.