Machine learning
主成分分析
主成分分析（Principal Component Analysis, PCA）是一种无监督降维方法——其现代教科书式处理可参见 Ian Jolliffe (2002) 的著作——该方法将高维数据压缩到更少的维度，同时尽可能保留方差。它将相关的变量重新表达为一组不相关的、按其捕获数据变异量多少排序的主成分。
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Method map
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来源
- Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI: 10.1007/b98835 ↗
如何引用本页
ScholarGate. (2026, June 1). Principal Component Analysis (PCA). ScholarGate. https://scholargate.app/zh/machine-learning/pca
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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