Vertaile menetelmiä

	Robust Gaussian Mixture Model	Robust Linear Regression
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2000	1964–1987
Kehittäjä	Peel, D. & McLachlan, G. J.	Huber, P. J.; Rousseeuw, P. J.
Tyyppi	Probabilistic clustering / density estimation	Outlier-resistant supervised regression
Alkuperäislähde	Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.
Rinnakkaisnimet	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	robust regression, M-estimator regression, Huber regression, outlier-resistant regression
Liittyvät	5	5
Tiivistelmä	Robust Gaussian Mixture Model replaces the standard Gaussian components with heavier-tailed distributions — most commonly Student's t-distributions — or incorporates trimming and down-weighting of outliers within the EM framework. The result is a probabilistic clustering and density-estimation method that assigns genuinely anomalous points less influence on component parameters, preventing outliers from distorting cluster shapes or positions.	Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.