So sánh phương pháp

	Hồi quy Lasso	Elastic Net	Phân tích thành phần chính
Lĩnh vực	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	1996	2005	2002
Người khởi xướng	Tibshirani, R.	Zou, H. & Hastie, T.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Loại	Regularized linear regression (L1 penalty)	Regularized linear regression (L1 + L2 penalty)	Unsupervised dimensionality reduction
Công trình gốc	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Tên gọi khác	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Liên quan	4	4	3
Tóm tắt	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.	Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.	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.