A Randomized PCA Forest is used to generate outlier scores for unsupervised anomaly detection, showing competitive or superior performance on benchmark datasets.
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.