Overview on kernels for least-squares support-vector-machine-based clustering: Explaining kernel spectral clustering

Abstract:

This letter presents an overview on some remarkable basics on kernels as well as the formulation of a clustering approach based on least-squares support vector machines. Specifically, the method known as kernel spectral clustering (KSC) is of interest. We explore the links between KSC and a weighted version of kernel principal component analysis (WKPCA). Also, we study the solution of the KSC problem by means of a primal-dual scheme. All mathematical developments are carried out following an entirely matrix formulation. As a result, in addition to the elegant KSC formulation, important insights and hints about the use and design of kernel-based approaches for clustering are provided.

Año de publicación:

2021

Keywords:

• Kernel spectral clustering KSC
• support vector machine (SVM)
• Clustering
• kernel principal component analysis

Fuente:

scopus