Machine learning
主成分回归 (PCR)
主成分回归首先将一组相关的预测变量压缩为少数几个主成分——即方差最大的方向——然后用这些主成分对响应变量进行回归。通过丢弃低方差方向，PCR在存在多重共线性（multicollinearity）和高维度的情况下稳定了估计，但代价是选择主成分时未参考响应变量。
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来源
- 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. DOI: 10.2307/2348005 ↗
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0
如何引用本页
ScholarGate. (2026, June 2). Principal Components Regression (PCR). ScholarGate. https://scholargate.app/zh/machine-learning/principal-components-regression
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