Comparer des méthodes

	Analyse en composantes principales	Régression Ridge
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2002	1970
Auteur d'origine	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Hoerl, A.E. & Kennard, R.W.
Type	Unsupervised dimensionality reduction	L2-regularized linear regression
Source fondatrice	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov 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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.