Group Anomaly Detection
3 papers with code • 0 benchmarks • 1 datasets
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Latest papers
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection
Existing approaches based on kernel mean embedding, which convert a point kernel to a distributional kernel, have two key issues: the point kernel employed has a feature map with intractable dimensionality; and it is {\em data independent}.
MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams
Given a stream of entries in a multi-aspect data setting i. e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner?
Group Anomaly Detection using Deep Generative Models
Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points.
