Randomized PCA Forest for Unsupervised Outlier Detection

2025/08/18 by Muhammad Rajabinasab, Farhad Pakdaman, Rajabinasab, Muhammad +7 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2508.12776

openalex publication_date 2025/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• An experimental study of existing tools for outlier detection and cleaning in trajectories
• Outlier detection by ensembling uncertainty with negative objectness
• D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments
• ADBench: Anomaly Detection Benchmark
• Explainable outlier detection through decision tree conditioning
• Extended Isolation Forest
• Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
• Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions

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• An Empirical Study of Feature Selection Granularity

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