Comparar métodos

	Análisis de Correlación Canónica	Regresión por Mínimos Cuadrados Parciales (PLS)
Campo	Estadística	Aprendizaje automático
Familia	Latent structure	Machine learning
Año de origen	1936	1975
Autor original	Harold Hotelling	Herman Wold; popularized by Svante Wold in chemometrics
Tipo	Multivariate linear dimension reduction and association	Supervised latent-variable regression
Fuente seminal	Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377.	Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.
Alias	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler
Relacionados	4	3
Resumen	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.	Partial least squares regression predicts a response from many, often highly collinear predictors by projecting them onto a small set of latent components — but, unlike principal components regression, it chooses those components to maximize their covariance with the response, not just the variance of the predictors. This supervised dimension reduction makes PLS a workhorse in chemometrics, spectroscopy, and other wide-data settings where predictors vastly outnumber observations.