পদ্ধতির তুলনা করুন

	প্রধান উপাদান বিশ্লেষণ	দৃঢ় রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	পরিসংখ্যান
পরিবার	Machine learning	Regression model
উদ্ভবের বছর	2002	1964
প্রবর্তক	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
ধরন	Unsupervised dimensionality reduction	Regression with outlier resistance
মৌলিক উৎস	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.
অপর নাম	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
সম্পর্কিত	3	6
সারসংক্ষেপ	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.