Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Robust Gaussian Mixture Model× | Isolation Forest× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2000 | 2008 |
| Autors≠ | Peel, D. & McLachlan, G. J. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tips≠ | Probabilistic clustering / density estimation | Unsupervised ensemble (random partitioning trees) |
| Pirmavots≠ | Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Citi nosaukumi≠ | Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Robust Gaussian Mixture Model replaces the standard Gaussian components with heavier-tailed distributions — most commonly Student's t-distributions — or incorporates trimming and down-weighting of outliers within the EM framework. The result is a probabilistic clustering and density-estimation method that assigns genuinely anomalous points less influence on component parameters, preventing outliers from distorting cluster shapes or positions. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
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