مقایسهٔ روش‌ها

	تحلیل مؤلفه‌های اصلی مقاوم (RPCA)	تحلیل مؤلفه‌های اصلی
حوزه	آمار	یادگیری ماشین
خانواده	Regression model	Machine learning
سال پیدایش	2011	2002
پدیدآور	Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
نوع	Robust dimensionality reduction / matrix decomposition	Unsupervised dimensionality reduction
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	3	3
خلاصه	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.