Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lokālā novirzes faktors (LOF)× | Vienas klases SVM× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2000 | 1999–2001 |
| Autors≠ | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Tips≠ | Density-based anomaly detection (unsupervised) | Anomaly / novelty detection (unsupervised) |
| Pirmavots≠ | 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. DOI ↗ | 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. DOI ↗ |
| Citi nosaukumi | LOF, local outlier factor, density-based outlier detection, local density deviation | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | 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. |
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