Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Isolation Forest× | Análisis de Componentes Principales× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2008 | 2002 |
| Autor original≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Tipo≠ | Unsupervised ensemble (random partitioning trees) | Unsupervised dimensionality reduction |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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