เปรียบเทียบวิธี

	Robust Principal Component Analysis (RPCA)	การวิเคราะห์องค์ประกอบหลัก	การถดถอยแบบทนทาน
สาขาวิชา	สถิติศาสตร์	การเรียนรู้ของเครื่อง	สถิติศาสตร์
ตระกูล	Regression model	Machine learning	Regression model
ปีกำเนิด	2011	2002	1964
ผู้ริเริ่ม	Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
ประเภท	Robust dimensionality reduction / matrix decomposition	Unsupervised dimensionality reduction	Regression with outlier resistance
แหล่งต้นตำรับ	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.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
ชื่อเรียกอื่น	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	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
ที่เกี่ยวข้อง	3	3	6
สรุป	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.	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.