A dependence maximization view of clustering
A dependence maximization view of clustering
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DOI：
10.1145/1273496.1273599
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发表时间：
2007-06
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影响因子：
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通讯作者：
Le Song;Alex Smola;A. Gretton;Karsten M. Borgwardt
中科院分区：
文献类型：
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作者：
Le Song;Alex Smola;A. Gretton;Karsten M. Borgwardt
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.
