قارن الطرق

	تحليل المكونات الرئيسية	الانحدار القوي
المجال	تعلم الآلة	الإحصاء
العائلة	Machine learning	Regression model
سنة النشأة	2002	1964
صاحب الطريقة	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
النوع	Unsupervised dimensionality reduction	Regression with outlier resistance
المصدر التأسيسي	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
الأسماء البديلة	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
ذات صلة	3	6
الملخص	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.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.