方法对比
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| 探索性因子分析（EFA）× | 主成分分析× | |
|---|---|---|
| 领域≠ | 统计学 | 机器学习 |
| 方法族≠ | Latent structure | Machine learning |
| 起源年份≠ | — | 2002 |
| 提出者≠ | — | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| 类型≠ | Latent variable / dimension reduction | Unsupervised dimensionality reduction |
| 开创性文献≠ | Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| 别名≠ | common factor analysis, açımlayıcı faktör analizi, factor analysis | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| 相关≠ | 4 | 3 |
| 摘要≠ | Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
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