手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 因子分析× | ガウス混合モデル× | |
|---|---|---|
| 分野≠ | 研究統計 | 機械学習 |
| 系統≠ | Process / pipeline | Machine learning |
| 提唱年≠ | 1931 | 1977 |
| 提唱者≠ | Louis Leon Thurstone | Dempster, Laird & Rubin (EM algorithm) |
| 種類≠ | Method | Probabilistic (soft) clustering — mixture model |
| 原典≠ | Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI ↗ | Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22. DOI ↗ |
| 別名≠ | EFA, CFA, latent variable modeling | Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians |
| 関連≠ | 3 | 4 |
| 概要≠ | 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. | A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation. |
| ScholarGateデータセット ↗ |
|
|