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| 요인 분석× | K-최근접 이웃× | |
|---|---|---|
| 분야≠ | 연구 통계 | 머신러닝 |
| 계열≠ | Process / pipeline | Machine learning |
| 기원 연도≠ | 1931 | 1967 |
| 창시자≠ | Louis Leon Thurstone | Cover, T.M. & Hart, P.E. |
| 유형≠ | Method | Instance-based (non-parametric) learning |
| 원전≠ | Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI ↗ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ |
| 별칭≠ | EFA, CFA, latent variable modeling | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning |
| 관련≠ | 3 | 5 |
| 요약≠ | Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data. | K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values. |
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