Using a Global Parameter for Gaussian Affinity Matrices in Spectral Clustering

• Published in: High Performance Computing for Computational Science - VECPAR 2008 pp. 378 - 390
• Main Authors: Mouysset, Sandrine, Noailles, Joseph, Ruiz, Daniel
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2008
• Series: Lecture Notes in Computer Science
• ISBN: 3540928588; 9783540928584
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-92859-1_34

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.