Fast Evolutionary Maximum Margin Clustering
Abstract
The maximum margin clustering approach is a recently proposed extension of the concept of support vector machines to the clustering problem. Briefly stated, it aims at finding an optimal partition of the data into two classes such that the margin induced by a subsequent application of a support vector machine is maximal. We propose a method based on stochastic search to address this hard optimization problem. While a direct implementation would be infeasible for large data sets, we present an efficient computational shortcut for assessing the ``quality'' of intermediate solutions. Experimental results show that our approach outperforms existing methods in terms of clustering accuracy.
Cite
Text
Gieseke et al. "Fast Evolutionary Maximum Margin Clustering." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553421Markdown
[Gieseke et al. "Fast Evolutionary Maximum Margin Clustering." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/gieseke2009icml-fast/) doi:10.1145/1553374.1553421BibTeX
@inproceedings{gieseke2009icml-fast,
title = {{Fast Evolutionary Maximum Margin Clustering}},
author = {Gieseke, Fabian and Pahikkala, Tapio and Kramer, Oliver},
booktitle = {International Conference on Machine Learning},
year = {2009},
pages = {361-368},
doi = {10.1145/1553374.1553421},
url = {https://mlanthology.org/icml/2009/gieseke2009icml-fast/}
}