Anomaly Detection and Prototype Selection Using Polyhedron Curvature

2020-04-05 · Benyamin Ghojogh, Fakhri Karray, Mark Crowley

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.

📄 PDF Abstract BibTeX arXiv:2004.02137

Code (1)

bghojogh/Curvature-Anomaly-Detection 공식 구현

Tasks

Anomaly Detection Denoising Image Denoising Out-of-Distribution DetectionPrototype Selection

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