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Conference Paper

A Dependence Maximization View of Clustering


Gretton,  A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Song, L., Smola, A., Gretton, A., & Borgwardt, K. (2007). A Dependence Maximization View of Clustering. In Z. Ghahramani (Ed.), ICML '07: 24th International Conference on Machine Learning (pp. 815-822). New York, NY, USA: ACM Press.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CD55-C
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.