Anomaly detection using principles of human perception. (arXiv:2103.12323v3 [cs.CR] UPDATED)

In the fields of statistics and unsupervised machine learning a fundamental
and well-studied problem is anomaly detection. Anomalies are difficult to
define, yet many algorithms have been proposed. Underlying the approaches is
the nebulous understanding that anomalies are rare, unusual or inconsistent
with the majority of data. The present work provides a philosophical treatise
to clearly define anomalies and develops an algorithm for their efficient
detection with minimal user intervention. Inspired by the Gestalt School of
Psychology and the Helmholtz …

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