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A dependence maximization view of clustering

Song, L; Smola, A; Gretton, A; Borgwardt, KM; (2007) A dependence maximization view of clustering. In: (pp. pp. 815-822).

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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: Proceedings paper
Title: A dependence maximization view of clustering
DOI: 10.1145/1273496.1273599
URI: http://discovery.ucl.ac.uk/id/eprint/1334330
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