September 28, 2025Open Access

Randomized PCA Forest for Outlier Detection

Key Points

The proposed method significantly improves outlier detection performance compared to classical methods.
Experimental results demonstrate superior accuracy over state-of-the-art approaches across several datasets.
The approach integrates randomized principal component analysis for enhanced computational efficiency.
High generalization power suggests it is a robust choice for unsupervised outlier detection tasks.

Abstract

We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Inspired by the performance of Randomized PCA (RPCA) Forest in approximate K-Nearest Neighbor (KNN) search, we develop a novel unsupervised outlier detection method that utilizes RPCA Forest for outlier detection. Experimental results showcase the superiority of the proposed approach compared to the classical and state-of-the-art methods in performing the outlier detection task on several datasets while performing competitively on the rest. The extensive analysis of the proposed method reflects it high generalization power and its computational efficiency, highlighting it as a good choice for unsupervised outlier detection.

Randomized PCA Forest for Outlier Detection

Key Points

Abstract

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