So sánh phương pháp

	Phân tích nhân tố	Phân tích thành phần chính	Bộ tự mã hóa biến phân
Lĩnh vực	Thống kê nghiên cứu	Học máy	Học sâu
Họ	Process / pipeline	Machine learning	Machine learning
Năm ra đời	1931	2002	2014
Người khởi xướng	Louis Leon Thurstone	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Kingma, D. P. & Welling, M.
Loại	Method	Unsupervised dimensionality reduction	Deep generative latent-variable model (encoder–decoder)
Công trình gốc	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Tên gọi khác	EFA, CFA, latent variable modeling	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Liên quan	3	3	5
Tóm tắt	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.	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.	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.