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| 로버스트 가우시안 프로세스× | 강건 선형 회귀× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2011 (formal treatment); GP foundations: Rasmussen & Williams 2006 | 1964–1987 |
| 창시자≠ | Jylanki, P.; Vanhatalo, J.; Vehtari, A. | Huber, P. J.; Rousseeuw, P. J. |
| 유형≠ | Probabilistic non-parametric regression / classification | Outlier-resistant supervised regression |
| 원전≠ | Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257. link ↗ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| 별칭 | Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP | robust regression, M-estimator regression, Huber regression, outlier-resistant regression |
| 관련 | 5 | 5 |
| 요약≠ | Robust Gaussian Process (Robust GP) extends the standard Gaussian Process framework by replacing the Gaussian noise likelihood with a heavy-tailed distribution — typically Student-t — so that outliers in the training data exert less influence on the learned function. It retains the full probabilistic, uncertainty-quantifying character of a standard GP while becoming far less sensitive to corrupted or anomalous observations. | 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. |
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