Comparer des méthodes

	Détection d'anomalies par autoencodeur robuste	SVM à une classe
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017	1999–2001
Auteur d'origine	Zhou, C. & Paffenroth, R. C.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Type	Unsupervised anomaly detection (robust deep learning)	Anomaly / novelty detection (unsupervised)
Source fondatrice	Zhou, C., & Paffenroth, R. C. (2017). Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 665–674). ACM.	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.
Alias	Robust Deep Autoencoder, Robust AE Anomaly Detection, RDAE, Robust Reconstruction-Based Anomaly Detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Apparentées	5	3
Résumé	Robust Autoencoder Anomaly Detection extends the standard autoencoder framework with robustness mechanisms — such as sparse decomposition, robust loss functions, or adversarial regularisation — so that the model learns a compact representation of normal behaviour while remaining resistant to the corrupting influence of anomalies embedded in the training data.	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.