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In the anomaly detection setting, the native feature embedding can be acrucial source of bias. We present a technique, Feature Omission using Contextin Unsupervised Settings (FOCUS) to learn a feature mapping that is invariantto changes exemplified in training sets while retaining as much descriptivepower as possible. While this method could apply to many unsupervised settings,we focus on applications in anomaly detection, where little task-labeled datais available. Our algorithm requires only non-anomalous sets of data, and doesnot require that the contexts in the training sets match the context of thetest set. By maximizing within-set variance and minimizing between-setvariance, we are able to identify and remove distracting features whileretaining fidelity to the descriptiveness needed at test time. In the linearcase, our formulation reduces to a generalized eigenvalue problem that can besolved quickly and applied to test sets outside the context of the trainingsets. This technique allows us to align technical definitions of anomalydetection with human definitions through appropriate mappings of the featurespace. We demonstrate that this method is able to remove uninformative parts ofthe feature space for the anomaly detection setting.

Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection