Clustering and Anomaly Detection with Kernels - Rojo-Álvarez, José Luis; Martínez-Ramón, Manel; Muñoz-Mar&iacute, Jordi; Camps-Valls, Gustau (Digital Signal Processing with Kernel Methods, 2018)
This chapter treats the relevant topic of clustering and anomaly detection with kernels. The field is in the core of machine learning, and has many practical implications. The chapter discusses the kernel‐based approaches systematically. They are clustering, density estimation (sometimes referred as to domain description), matched subspace detectors, anomaly change detection, and statistical hypothesis testing. Kernel clustering is based on reformulating existing clustering methods with kernels. Such reformulation, nevertheless, can take two different pathways: either “kernelize” a standard clustering algorithm that relies solely on dot products between samples or that relies on distances between samples. As an alternative to the previous approaches for clustering with kernels, the description of the domain can be done via support vectors. This idea leads to several algorithms, such as the one‐class support vector machine (OC‐SVM) and the related support vector domain description (SVDD).