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  A Dependence Maximization View of Clustering

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CD55-C Version Permalink: https://hdl.handle.net/21.11116/0000-0003-E23A-D
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1273496.1273599 (Publisher version)
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 Creators:
Song, L, Author
Smola, AJ, Author           
Gretton, A1, 2, Author           
Borgwardt, KM, Author           
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: 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.

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 Dates: 2007-06
 Publication Status: Issued
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 Identifiers: DOI: 10.1145/1273496.1273599
BibTex Citekey: 4471
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Title: Twenty-Fourth Annual International Conference on Machine Learning (ICML 2007)
Place of Event: Corvallis, OR, USA
Start-/End Date: 2007-06-20 - 2007-06-24

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Title: ICML '07: 24th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Ghahramani, Z, Editor
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 815 - 822 Identifier: ISBN: 978-1-59593-793-3