방법 비교

	강건 주성분 분석 (RPCA)	Robust Regression
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	2011	1964
창시자	Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
유형	Robust dimensionality reduction / matrix decomposition	Regression with outlier resistance
원전	Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
별칭	RPCA, robust principal component analysis, low-rank plus sparse decomposition, Robust Temel Bileşen Analizi (RPCA)	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
관련	3	6
요약	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.	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.