Сравнение методов

	Локальный фактор выбросов (Local Outlier Factor, LOF)	Автоэнкодер	Одноклассовая SVM
Область	Машинное обучение	Глубокое обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	2000	2006	1999–2001
Автор метода	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Hinton, G.E. & Salakhutdinov, R.R.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Тип	Density-based anomaly detection (unsupervised)	Neural network (encoder-decoder)	Anomaly / novelty detection (unsupervised)
Основополагающий источник	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.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	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.
Другие названия	LOF, local outlier factor, density-based outlier detection, local density deviation	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Связанные	4	4	3
Сводка	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.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the 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.