قارن الطرق

	تحليل المكونات الرئيسية القوي (RPCA)	تحليل المكونات الرئيسية
المجال	الإحصاء	تعلم الآلة
العائلة	Regression model	Machine learning
سنة النشأة	2011	2002
صاحب الطريقة	Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
النوع	Robust dimensionality reduction / matrix decomposition	Unsupervised dimensionality reduction
المصدر التأسيسي	Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
الأسماء البديلة	RPCA, robust principal component analysis, low-rank plus sparse decomposition, Robust Temel Bileşen Analizi (RPCA)	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
ذات صلة	3	3
الملخص	Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part.	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.