Name
Abstract | ||
|---|---|---|
Traditional spectral-based methods such as PCA are popular for anomaly detection in a variety of problems and domains. However, if data includes tensor (multiway) structure (e.g. space-time-measurements), some meaningful anomalies may remain invisible with these methods. Although tensor-based anomaly detection (TAD) has been applied within a variety of disciplines over the last twenty years, it is not yet recognized as a formal category in anomaly detection. This survey aims to highlight the potential of tensor-based techniques as a novel approach for detection and identification of abnormalities and failures. We survey the interdisciplinary works in which TAD is reported and characterize the learning strategies, methods and applications; extract the important open issues in TAD and provide the corresponding existing solutions according to the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.knosys.2016.01.027 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Anomaly detection,Tensor analysis,Multiway data,Tensor decomposition,Tensorial learning | Journal | 98 |
Issue | ISSN | Citations |
C | 0950-7051 | 5 |
PageRank | References | Authors |
0.41 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hadi Fanaee-T | 1 | 75 | 8.55 |
| João Gama | 2 | 3785 | 271.37 |