เปรียบเทียบวิธี

	Factor Analysis	แบบจำลองส่วนผสมแบบเกาส์เซียน	การวิเคราะห์องค์ประกอบหลัก
สาขาวิชา	สถิติการวิจัย	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Process / pipeline	Machine learning	Machine learning
ปีกำเนิด	1931	1977	2002
ผู้ริเริ่ม	Louis Leon Thurstone	Dempster, Laird & Rubin (EM algorithm)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
ประเภท	Method	Probabilistic (soft) clustering — mixture model	Unsupervised dimensionality reduction
แหล่งต้นตำรับ	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
ชื่อเรียกอื่น	EFA, CFA, latent variable modeling	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
ที่เกี่ยวข้อง	3	4	3
สรุป	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.	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.