论文标题
使用多面体曲率选择异常检测和原型选择
Anomaly Detection and Prototype Selection Using Polyhedron Curvature
论文作者
论文摘要
我们根据多面体曲率的概念提出了一种新型的异常检测方法，称为曲率异常检测（CAD）和核CAD。将最近的邻居使用一个点，我们将每个数据点视为多面体的顶点，其中较异常的点具有更大的曲率。我们还提出了逆CAD（ICAD）和内核ICAD，例如，从相反的角度查看CAD，例如排名和原型选择。我们定义了异常景观和异常路径的概念，并演示了它的应用，这是图像降级。所提出的方法很简单且易于实现。我们对不同基准测试的实验表明，所提出的方法对于异常检测和原型选择有效。
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.
