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.
| Author(s): | Song, L. and Smola, AJ. and Gretton, A. and Borgwardt, KM. |
| Journal: | Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007) |
| Pages: | 815-822 |
| Year: | 2007 |
| Month: | June |
| Day: | 0 |
| Editors: | Ghahramani, Z. |
| Publisher: | ACM Press |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | New York, NY, USA |
| DOI: | 10.1145/1273496.1273599 |
| Event Name: | Twenty-Fourth Annual International Conference on Machine Learning (ICML 2007) |
| Event Place: | Corvallis, OR, USA |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 978-1-59593-793-3 |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{4471,
title = {A Dependence Maximization View of Clustering},
journal = {Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007)},
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.},
pages = {815-822},
editors = {Ghahramani, Z. },
publisher = {ACM Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {New York, NY, USA},
month = jun,
year = {2007},
slug = {4471},
author = {Song, L. and Smola, AJ. and Gretton, A. and Borgwardt, KM.},
month_numeric = {6}
}