Randomized PCA Forest for Unsupervised Outlier Detection

TL;DR

This paper introduces a new unsupervised outlier detection method leveraging Randomized PCA Forest, demonstrating superior performance and efficiency over existing techniques across multiple datasets.

Contribution

It develops a novel outlier detection approach based on RPCA Forest, extending its application from KNN search to outlier detection with robust and efficient results.

Findings

Outperforms classical and state-of-the-art outlier detection methods on several datasets.

Shows robustness and computational efficiency in various experimental settings.

Provides extensive analysis confirming its effectiveness for unsupervised outlier detection.

Abstract

We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated 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 unsupervised outlier detection by deriving an outlier score from its intrinsic properties. 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 its robustness and its computational efficiency, highlighting it as a good choice for unsupervised outlier detection.