Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information.
Zhiyuan LiuChunjie CaoJingzhang SunPublished in: CoRR (2022)
Keyphrases
- multi view
- anomaly detection
- semi supervised
- multi view learning
- unsupervised anomaly detection
- depth map
- three dimensional
- single view
- intrusion detection
- anomalous behavior
- co training
- active learning
- multiple views
- unsupervised learning
- d objects
- multiple viewpoints
- multi view clustering
- viewpoint
- labeled data
- supervised learning
- label information
- semi supervised classification
- similarity measure
- information extraction
- motion estimation