Порівняння методів

	Метод головних компонент	Моделювання структурними рівняннями (SEM)
Галузь	Машинне навчання	Статистика
Родина	Machine learning	Latent structure
Рік появи	2002	1970
Автор методу	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Karl Jöreskog (LISREL framework, 1970s)
Тип	Unsupervised dimensionality reduction	Latent variable / causal modeling
Основоположне джерело	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Інші назви	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Пов'язані	3	5
Підсумок	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.	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.