# Efficient Anomaly Detection for High-Dimensional Sensing Data With One-Class Support Vector Machine

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/efficient-anomaly-detection-for-high-dimensional-sensing-data-with-one-class/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Yan Qiao,Kui Wu,Peng Jin |
| Citations | 57 |
| DOI | 10.1109/tkde.2021.3077046 |
| Fields | Computer Science,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3159922383 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Yan Qiao](https://scholariq.org/researchers/yan-qiao/)

## Paper primary topic

- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)

## Paper topics

- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)
- [Network Security and Intrusion Detection](https://scholariq.org/topics/network-security-and-intrusion-detection/)
- [Water Systems and Optimization](https://scholariq.org/topics/water-systems-and-optimization/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.