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ICML
2007
IEEE

A dependence maximization view of clustering

10 years 2 months ago
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.
Le Song, Alexander J. Smola, Arthur Gretton, Karst
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt
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