Salīdzināt metodes

	Faktoru analīze	Hierarhiskā klasterizācija	LASSO regresija
Nozare	Pētniecības statistika	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Process / pipeline	Machine learning	Machine learning
Izcelsmes gads	1931	1963	1996
Autors	Louis Leon Thurstone	Ward, J. H.	Tibshirani, R.
Tips	Method	Unsupervised clustering (agglomerative)	Regularized linear regression (L1 penalty)
Pirmavots	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Citi nosaukumi	EFA, CFA, latent variable modeling	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Saistītās	3	4	4
Kopsavilkums	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.