Summary of Anomaly Detection Based on Isolation Mechanisms: a Survey, by Yang Cao et al.
Anomaly Detection Based on Isolation Mechanisms: A Survey
by Yang Cao, Haolong Xiang, Hang Zhang, Ye Zhu, Kai Ming Ting
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper reviews state-of-the-art isolation-based methods for unsupervised anomaly detection in large-scale, high-dimensional, and heterogeneous data. It highlights the efficiency and performance advantages of these methods, including low computational complexity, low memory usage, high scalability, robustness to noise and irrelevant features, and no need for prior knowledge or heavy parameter tuning. The paper also discusses extensions and applications of isolation-based methods in scenarios such as detecting anomalies in streaming data, time series, trajectory, and image datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research looks at a new way to find unusual patterns in big data. Anomaly detection is important because it can help us identify problems or threats before they cause harm. The team reviews different approaches that are good at finding these anomalies quickly and efficiently. They also talk about how these methods can be used in different situations, like tracking changes over time or looking for weird images. |
Keywords
* Artificial intelligence * Anomaly detection * Time series * Tracking * Unsupervised
