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Reconstruction Error-based Anomaly Detection with Few Outlying Examples
It consists in training an Autoencoder to reconstruct a set of examples deemed to represent the normality and then to point out as anomalies those data that show a sufficiently large reconstruction error.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
CFOF: A Concentration Free Measure for Anomaly Detection
As a main contribution, we formalize the notion of concentration of outlier scores and theoretically prove that CFOF does not concentrate in the Euclidean space for any arbitrary large dimensionality.
On the Tractability of Minimal Model Computation for Some CNF Theories
Therefore, a specific eliminating operator is defined by which the GEA computes, in polynomial time, a minimal model for a class of CNF that strictly includes head-elementary-set-free (HEF) CNF theories [GLL06], which form, in their turn, a strict superset of HCF theories.
Outlying Property Detection with Numerical Attributes
We introduce a measure to quantify the degree the outlierness of an object, which is associated with the relative likelihood of the value, compared to the to the relative likelihood of other objects in the database.
