Salīdzināt metodes

	UMAP	Faktoru analīze	K-means klasterizācija
Nozare	Mašīnmācīšanās	Pētniecības statistika	Mašīnmācīšanās
Saime	Machine learning	Process / pipeline	Machine learning
Izcelsmes gads	2018	1931	1967 (formalized 1982)
Autors	McInnes, L.; Healy, J.; Melville, J.	Louis Leon Thurstone	MacQueen, J. B.; Lloyd, S. P.
Tips	Nonlinear manifold-learning dimension reduction	Method	Partitional clustering
Pirmavots	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Citi nosaukumi	UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction	EFA, CFA, latent variable modeling	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Saistītās	5	3	4
Kopsavilkums	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.	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.