Using a Global Parameter for Gaussian Affinity Matrices in Spectral Clustering

Authors:
Sandrine Mouysset;Joseph Noailles;Daniel Ruiz
Affiliations:
IRIT-ENSEEIHT, University of Toulouse, France;IRIT-ENSEEIHT, University of Toulouse, France;IRIT-ENSEEIHT, University of Toulouse, France
Venue:
High Performance Computing for Computational Science - VECPAR 2008
Year:
2008

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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.