Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection

A new embedding is proposed using a set of locally varying data projections, with each projection responsible for persever-ing the variations that distinguish a local cluster of instances from all other instances, while simultaneously allowing the probability that an instance belongs to a cluster to be statistically inferred from the one-dimensional, local projection associated with the cluster.