So sánh phương pháp

	Phân tích thành phần chính	Ridge Regression
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2002	1970
Người khởi xướng	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Hoerl, A.E. & Kennard, R.W.
Loại	Unsupervised dimensionality reduction	L2-regularized linear regression
Công trình gốc	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.
Tên gọi khác	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Liên quan	3	4
Tóm tắt	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.