Σύγκριση μεθόδων

	Παλινδρόμηση Κυρίων Συνιστωσών (PCR)	Παλινδρόμηση Ελάχιστων Τετραγώνων (PLS)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1982	1975
Δημιουργός	Principal-component regression literature (Jolliffe and others)	Herman Wold; popularized by Svante Wold in chemometrics
Τύπος	Unsupervised dimension reduction + regression	Supervised latent-variable regression
Θεμελιώδης πηγή	Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 31(3), 300–303.	Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.
Εναλλακτικές ονομασίες	PCR, PCA regression, temel bileşenler regresyonu	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler
Συναφείς	3	3
Σύνοψη	Principal components regression first compresses a set of correlated predictors into a few principal components — the directions of greatest variance — and then regresses the response on those components. By discarding low-variance directions, PCR stabilizes estimation in the presence of multicollinearity and high dimensionality, at the cost of choosing components without reference to the response.	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.