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| Regularized Gaussian Mixture Model× | One-Class SVM× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2000s–2010s | 1999–2001 |
| Urheber≠ | Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter) | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Typ≠ | Probabilistic clustering with regularization | Anomaly / novelty detection (unsupervised) |
| Wegweisende Quelle≠ | Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631. DOI ↗ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ |
| Aliasnamen | Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations. | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. |
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