Comparer des méthodes

	Analyse en composantes principales	Régression Lasso
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2002	1996
Auteur d'origine	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Tibshirani, R.
Type	Unsupervised dimensionality reduction	Regularized linear regression (L1 penalty)
Source fondatrice	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Apparentées	3	4
Résumé	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.	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.