Σύγκριση μεθόδων

	Επαρκής Ανάλυση Παραγόντων	Διαγνωστικά Επιρροής (Απόσταση Cook, DFFITS, Μοχλός)	Ανάλυση Κύριων Συνιστωσών
Πεδίο	Στατιστική	Στατιστική	Μηχανική Μάθηση
Οικογένεια	Regression model	Regression model	Machine learning
Έτος προέλευσης	2003	1977	2002
Δημιουργός	Pison, Rousseeuw, Filzmoser & Croux	R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Τύπος	Robust latent-factor model	Regression diagnostic	Unsupervised dimensionality reduction
Θεμελιώδης πηγή	Pison, G., Rousseeuw, P. J., Filzmoser, P., & Croux, C. (2003). Robust factor analysis. Journal of Multivariate Analysis, 84(1), 145-172.	Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Εναλλακτικές ονομασίες	robust factor analysis, outlier-resistant factor analysis, MCD-based factor analysis, Robust Faktör Analizi	Cook's distance, DFFITS, leverage, influential observation detection	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Συναφείς	5	5	3
Σύνοψη	Robust Factor Analysis recovers the latent factor structure of multivariate continuous data while resisting the distorting pull of outliers. Introduced by Pison, Rousseeuw, Filzmoser and Croux (2003), it replaces the classical sample covariance with a robust estimator such as the Minimum Covariance Determinant (MCD) or an S-estimator before extracting factors.	Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients.	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.