Using a global parameter for Gaussian affinity matrices in spectral clustering

Abstract

Clustering aims to partition a data set by bringing together similar elements in subsets. Spectral clustering consists in selecting dominant eigenvectors of a matrix called affinity matrix in order to define a low-dimensional data space in which data points are easy to cluster. The key is to design a good affinity matrix. If we consider the usual Gaussian affinity matrix, it depends on a scaling parameter which is difficult to select. Our goal is to grasp the influence of this parameter and to propose an expression with a reasonable computational cost. © 2008 Springer Berlin Heidelberg.