A dependence maximization view of clustering

We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Independence Criterion (HSIC). Under this framework, we unify the geometric, spectral, and statistical dependence views of clustering, and subsume many existing algorithms as special cases (e.g. k-means and spectral clustering). Distinctive to our framework is that kernels can also be applied on the labels, which can endow them with particular structures. We also obtain a perturbation bound on the change in k-means clustering.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt
• Keywords: Hilbert-Schmidt Independence Criterion, ICML 2007, Machine Learning, Spectral Clustering, Statistical Dependence Views