방법 비교

	요인 분석	Variational Autoencoder
분야	연구 통계	딥러닝
계열	Process / pipeline	Machine learning
기원 연도	1931	2014
창시자	Louis Leon Thurstone	Kingma, D. P. & Welling, M.
유형	Method	Deep generative latent-variable model (encoder–decoder)
원전	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
별칭	EFA, CFA, latent variable modeling	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
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.	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.