# Anomaly Detection in High Dimensional Data

arXiv: https://arxiv.org/abs/1908.04000 · HTML: https://mlsanity.com/paper/1908.04000/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Quantum Normalizing Flows for Anomaly Detection](https://mlsanity.com/paper/2402.02866/) (2024)
- [Unsupervised Anomaly Detection Ensembles using Item Response Theory](https://mlsanity.com/paper/2106.06243/) (2021)
- [UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring](https://mlsanity.com/paper/2111.10010/) (2021)
- [Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems](https://mlsanity.com/paper/2304.02947/) (2023)
- [Unsupervised Anomaly Detection via Nonlinear Manifold Learning](https://mlsanity.com/paper/2306.09441/) (2023)
- [Distribution and volume based scoring for Isolation Forests](https://mlsanity.com/paper/2309.11450/) (2023)
- [Online-Adaptive Anomaly Detection for Defect Identification in Aircraft Assembly](https://mlsanity.com/paper/2406.12698/) (2024)

## Abstract (arXiv)

The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. In this article, we propose an algorithm that addresses these limitations. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation. Using various synthetic and real datasets, we demonstrate the wide applicability and usefulness of our algorithm, which we call the stray algorithm. We also demonstrate how this algorithm can assist in detecting anomalies present in other data structures using feature engineering. We show the situations where the stray algorithm outperforms the HDoutliers algorithm both in accuracy and computational time. This framework is implemented in the open source R package stray.