Võrdle meetodeid

	Isejuhitav isolatsioonimets (Self-supervised Isolation Forest)	Kohaliku Outlier Tegur (LOF)
Valdkond	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	2008–2020s	2000
Looja	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.
Tüüp	Ensemble anomaly detector with self-supervised pre-training	Density-based anomaly detection (unsupervised)
Algallikas	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.	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.
Rööpnimetused	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	LOF, local outlier factor, density-based outlier detection, local density deviation
Seotud	4	4
Kokkuvõte	Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.	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.