Anomaly detection is a one-class classification (OCC) problem where the methods learn
either a generative model of the inlier class (eg, in the variants of kernel principal component …

Abnormality detection in medical images is a one-class classification problem for which
existing methods typically involve variants of kernel principal component analysis or one …

Dimensionality reduction (DR) on the manifold includes effective methods which project the
data from an implicit relational space onto a vectorial space. Regardless of the …

Quantifying the abnormal degree of each instance within data sets to detect outlying
instances, is an issue in unsupervised anomaly detection research. In this paper, we …

Kernel principal component analysis (KPCA) is a powerful tool for nonlinear feature
extraction, but its standard formulation is not well-suited for streaming data. Although there …

Coastal communities face unique challenges due to their increased vulnerability and
exposure to natural hazards, with humans and their assets being at a close proximity to the …

Finding patterns in data that defy expected behavior is what anomaly detection entails. In
many application fields, these incorrect patterns are referred to as contaminants …

This thesis focuses on anomaly detection, a classification task that involves identifying
whether an image is normal/inlier or anomalous/outlier. Learning schemes typically train on …