Comparer des méthodes

	Analyse factorielle	Autoencodeur Variationnel
Domaine	Statistiques de recherche	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	1931	2014
Auteur d'origine	Louis Leon Thurstone	Kingma, D. P. & Welling, M.
Type	Method	Deep generative latent-variable model (encoder–decoder)
Source fondatrice	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).
Alias	EFA, CFA, latent variable modeling	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Apparentées	3	5
Résumé	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.