We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.
| Author(s): | Wu, M. and Schölkopf, B. |
| Book Title: | Advances in Neural Information Processing Systems 19 |
| Journal: | Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference |
| Pages: | 1529-1536 |
| Year: | 2007 |
| Month: | September |
| Day: | 0 |
| Editors: | B Sch{\"o}lkopf and J Platt and T Hofmann |
| Publisher: | MIT Press |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Cambridge, MA, USA |
| Event Name: | 20th Annual Conference on Neural Information Processing Systems (NIPS 2006) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 0-262-19568-2 |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{4149,
title = {A Local Learning Approach for Clustering},
journal = {Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference},
booktitle = {Advances in Neural Information Processing Systems 19},
abstract = {We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.},
pages = {1529-1536},
editors = {B Sch{\"o}lkopf and J Platt and T Hofmann},
publisher = {MIT Press},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Cambridge, MA, USA},
month = sep,
year = {2007},
slug = {4149},
author = {Wu, M. and Sch{\"o}lkopf, B.},
month_numeric = {9}
}