手法を比較

	主成分分析	因子分析	Lasso回帰
分野	機械学習	研究統計	機械学習
系統	Machine learning	Process / pipeline	Machine learning
提唱年	2002	1931	1996
提唱者	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Louis Leon Thurstone	Tibshirani, R.
種類	Unsupervised dimensionality reduction	Method	Regularized linear regression (L1 penalty)
原典	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
別名	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	EFA, CFA, latent variable modeling	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
関連	3	3	4
概要	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.	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.	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.