Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Isolation Forest× | Primārā komponentu analīze× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2008 | 2002 |
| Autors≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Tips≠ | Unsupervised ensemble (random partitioning trees) | Unsupervised dimensionality reduction |
| Pirmavots≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Citi nosaukumi≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
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