# Randomized PCA Forest for Unsupervised Outlier Detection

arXiv: https://arxiv.org/abs/2508.12776 · HTML: https://mlsanity.com/paper/2508.12776/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [Outlier detection by ensembling uncertainty with negative objectness](https://mlsanity.com/paper/2402.15374/) (2024)
- [D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments](https://mlsanity.com/paper/2210.08212/) (2022)
- [Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version](https://mlsanity.com/paper/2501.09591/) (2025)
- [Explainable outlier detection through decision tree conditioning](https://mlsanity.com/paper/2001.00636/) (2020)

## Built on by (most cited first, 2 of 2)

- [An Empirical Study of Feature Selection Granularity](https://mlsanity.com/paper/2607.24145/) (2026)
- [Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version](https://mlsanity.com/paper/2608.12057/) (2026)

## Abstract (arXiv)

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.