Сравнение на методи

	Robust Gaussian Mixture Model	Robust Linear Regression
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2000	1964–1987
Създател	Peel, D. & McLachlan, G. J.	Huber, P. J.; Rousseeuw, P. J.
Тип	Probabilistic clustering / density estimation	Outlier-resistant supervised regression
Основополагащ източник	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.
Други названия	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	robust regression, M-estimator regression, Huber regression, outlier-resistant regression
Свързани	5	5
Резюме	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.