A Randomized PCA Forest is used to generate outlier scores for unsupervised anomaly detection, showing competitive or superior performance on benchmark datasets.
Asynchronism-based principal component analysis for time series data mining,
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Randomized PCA Forest for Unsupervised Outlier Detection
A Randomized PCA Forest is used to generate outlier scores for unsupervised anomaly detection, showing competitive or superior performance on benchmark datasets.