Compara mètodes

	Anàlisi Factorial	Variational Autoencoder
Camp	Estadística per a la recerca	Aprenentatge profund
Família	Process / pipeline	Machine learning
Any d'origen	1931	2014
Autor original	Louis Leon Thurstone	Kingma, D. P. & Welling, M.
Tipus	Method	Deep generative latent-variable model (encoder–decoder)
Font seminal	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).
Àlies	EFA, CFA, latent variable modeling	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Relacionats	3	5
Resum	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.