Módszerek összehasonlítása

	Faktoranalízis	K-means klaszterezés	Variációs Autoencoder
Tudományterület	Kutatási statisztika	Gépi tanulás	Mélytanulás
Módszercsalád	Process / pipeline	Machine learning	Machine learning
Keletkezés éve	1931	1967 (formalized 1982)	2014
Megalkotó	Louis Leon Thurstone	MacQueen, J. B.; Lloyd, S. P.	Kingma, D. P. & Welling, M.
Típus	Method	Partitional clustering	Deep generative latent-variable model (encoder–decoder)
Alapmű	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).
Alternatív nevek	EFA, CFA, latent variable modeling	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model
Kapcsolódó	3	4	5
Összefoglaló	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.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	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.