# Meta-survey on outlier and anomaly detection

arXiv: https://arxiv.org/abs/2312.07101 · HTML: https://mlsanity.com/paper/2312.07101/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 74 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://mlsanity.com/paper/2110.14051/) (2021)
- [One-Class Classification: Taxonomy of Study and Review of Techniques](https://mlsanity.com/paper/1312.0049/) (2013)
- [Anomaly Detection by Robust Statistics](https://mlsanity.com/paper/1707.09752/) (2017)
- [On the Nature and Types of Anomalies: A Review of Deviations in Data](https://mlsanity.com/paper/2007.15634/) (2020)

## Built on by (most cited first, 13 of 13)

- [We Need to Rethink Benchmarking in Anomaly Detection](https://mlsanity.com/paper/2507.15584/) (2025)
- [Unsupervised Surrogate Anomaly Detection](https://mlsanity.com/paper/2504.20733/) (2025)
- [Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices](https://mlsanity.com/paper/2506.04553/) (2025)
- [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://mlsanity.com/paper/2603.17570/) (2026)
- [Deep Transductive Outlier Detection](https://mlsanity.com/paper/2404.03495/) (2024)
- [Tighten The Lasso: A Convex Hull Volume-based Anomaly Detection Method](https://mlsanity.com/paper/2502.18601/) (2025)
- [Explainable Unsupervised Anomaly Detection with Random Forest](https://mlsanity.com/paper/2504.16075/) (2025)
- [Unsupervised Symbolic Anomaly Detection](https://mlsanity.com/paper/2603.17575/) (2026)
- [Polyra Swarms: A Shape-Based Approach to Machine Learning](https://mlsanity.com/paper/2506.13217/) (2025)
- [RangeAD: Fast On-Model Anomaly Detection](https://mlsanity.com/paper/2603.17795/) (2026)
- [A Modular LLM Framework for Explainable Price Outlier Detection](https://mlsanity.com/paper/2603.20636/) (2026)
- [Why Ranking Anomaly Detection Algorithms Isn't as Reliable as You May Think](https://mlsanity.com/paper/2608.04613/) (2026)
- [Moment-Based Anomaly Detection in High-Dimensional Mixed Data](https://mlsanity.com/paper/2610.16285/) (2026)

## Abstract (arXiv)

The impact of outliers and anomalies on model estimation and data processing is of paramount importance, as evidenced by the extensive body of research spanning various fields over several decades: thousands of research papers have been published on the subject. As a consequence, numerous reviews, surveys, and textbooks have sought to summarize the existing literature, encompassing a wide range of methods from both the statistical and data mining communities. While these endeavors to organize and summarize the research are invaluable, they face inherent challenges due to the pervasive nature of outliers and anomalies in all data-intensive applications, irrespective of the specific application field or scientific discipline. As a result, the resulting collection of papers remains voluminous and somewhat heterogeneous. To address the need for knowledge organization in this domain, this paper implements the first systematic meta-survey of general surveys and reviews on outlier and anomaly detection. Employing a classical systematic survey approach, the study collects nearly 500 papers using two specialized scientific search engines. From this comprehensive collection, a subset of 56 papers that claim to be general surveys on outlier detection is selected using a snowball search technique to enhance field coverage. A meticulous quality assessment phase further refines the selection to a subset of 25 high-quality general surveys. Using this curated collection, the paper investigates the evolution of the outlier detection field over a 20-year period, revealing emerging themes and methods. Furthermore, an analysis of the surveys sheds light on the survey writing practices adopted by scholars from different communities who have contributed to this field. Finally, the paper delves into several topics where consensus has emerged from the literature. These include taxonomies of outlier types, challenges posed by high-dimensional data, the importance of anomaly scores, the impact of learning conditions, difficulties in benchmarking, and the significance of neural networks. Non-consensual aspects are also discussed, particularly the distinction between local and global outliers and the challenges in organizing detection methods into meaningful taxonomies.