方法对比

	主成分回归 (PCR)	偏最小二乘回归 (PLS)
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	1982	1975
提出者	Principal-component regression literature (Jolliffe and others)	Herman Wold; popularized by Svante Wold in chemometrics
类型	Unsupervised dimension reduction + regression	Supervised latent-variable regression
开创性文献	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.
别名	PCR, PCA regression, temel bileşenler regresyonu	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler
相关	3	3
摘要	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.