Сравнение методов

	Isolation Forest	Анализ главных компонент
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2008	2002
Автор метода	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Тип	Unsupervised ensemble (random partitioning trees)	Unsupervised dimensionality reduction
Основополагающий источник	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Другие названия	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Связанные	5	3
Сводка	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.