Anomaly Detection and Prototype Selection Using Polyhedron Curvature
This work addresses anomaly detection and prototype selection problems for data analysis, but it appears incremental as it builds on existing nearest-neighbor and curvature concepts.
The authors tackled anomaly detection and prototype selection by introducing Curvature Anomaly Detection (CAD) and its variants based on polyhedron curvature, with results showing effectiveness on benchmarks.
We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature. Using the nearest neighbors for a point, we consider every data point as the vertex of a polyhedron where the more anomalous point has more curvature. We also propose inverse CAD (iCAD) and Kernel iCAD for instance ranking and prototype selection by looking at CAD from an opposite perspective. We define the concept of anomaly landscape and anomaly path and we demonstrate an application for it which is image denoising. The proposed methods are straightforward and easy to implement. Our experiments on different benchmarks show that the proposed methods are effective for anomaly detection and prototype selection.