Porovnať metódy

	UMAP	Faktorová analýza	Analýza hlavných komponentov
Odbor	Strojové učenie	Štatistika vo výskume	Strojové učenie
Rodina	Machine learning	Process / pipeline	Machine learning
Rok vzniku	2018	1931	2002
Tvorca	McInnes, L.; Healy, J.; Melville, J.	Louis Leon Thurstone	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Nonlinear manifold-learning dimension reduction	Method	Unsupervised dimensionality reduction
Pôvodný zdroj	McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Ďalšie názvy	UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction	EFA, CFA, latent variable modeling	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Príbuzné	5	3	3
Zhrnutie	UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis.	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.