Σύγκριση μεθόδων

	Τοπικός Παράγοντας Εκτός Τροχιάς (LOF)	One-Class SVM
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2000	1999–2001
Δημιουργός	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Τύπος	Density-based anomaly detection (unsupervised)	Anomaly / novelty detection (unsupervised)
Θεμελιώδης πηγή	Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104.	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.
Εναλλακτικές ονομασίες	LOF, local outlier factor, density-based outlier detection, local density deviation	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Συναφείς	4	3
Σύνοψη	Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space.	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.