Machine learning
偏最小二乘回归 (PLS)
偏最小二乘回归通过将大量通常高度共线的预测变量投影到少数几个潜在成分上，来预测响应变量。但与主成分回归不同的是，它选择这些成分是为了最大化它们与响应变量的协方差，而不仅仅是预测变量的方差。这种有监督的降维使得PLS成为化学计量学、光谱学以及预测变量远超观测数量的宽数据集（wide-data）设置中的主力方法。
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来源
- Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. DOI: 10.1016/S0169-7439(01)00155-1 ↗
- Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1–17. DOI: 10.1016/0003-2670(86)80028-9 ↗
如何引用本页
ScholarGate. (2026, June 2). Partial Least Squares Regression (PLS). ScholarGate. https://scholargate.app/zh/machine-learning/partial-least-squares
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