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A Dependence Maximization View of Clustering

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84953

Smola,  AJ
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75313

Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Song, L., Smola, A., Gretton, A., & Borgwardt, K. (2007). A Dependence Maximization View of Clustering. In Twenty-Fourth Annual International Conference on Machine Learning (ICML 2007) (pp. 815-822). New York, NY, USA: ACM Press.


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