So sánh phương pháp

	One-Class SVM	Local Outlier Factor (LOF)
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1999–2001	2000
Người khởi xướng	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.
Loại	Anomaly / novelty detection (unsupervised)	Density-based anomaly detection (unsupervised)
Công trình gốc	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.	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.
Tên gọi khác	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM	LOF, local outlier factor, density-based outlier detection, local density deviation
Liên quan	3	4
Tóm tắt	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.	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.