  Human analysts that use anomaly detection systems in practice want to retain
the use of simple and explainable global anomaly detectors. In this paper, we
propose a novel human-in-the-loop learning algorithm called GLAD (GLocalized
Anomaly Detection) that supports global anomaly detectors. GLAD automatically
learns their local relevance to specific data instances using label feedback
from human analysts. The key idea is to place a uniform prior on the relevance
of each member of the anomaly detection ensemble over the input feature space
via a neural network trained on unlabeled instances. Subsequently, weights of
the neural network are tuned to adjust the local relevance of each ensemble
member using all labeled instances. GLAD also provides explanations which can
improve the understanding of end-users about anomalies. Our experiments on
synthetic and real-world data show the effectiveness of GLAD in learning the
local relevance of ensemble members and discovering anomalies via label
feedback.
