Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering
Abstract
In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to the off-diagonal entries. This problem, referred to as diagonal dominance, often occurs when certain kernel functions are applied to sparse high-dimensional data, such as text corpora. In this paper we investigate the implications of diagonal dominance for unsupervised kernel methods, specifically in the task of document clustering. We propose a selection of strategies for addressing this issue, and evaluate their effectiveness in producing more accurate and stable clusterings.
Cite
Text
Greene and Cunningham. "Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143892Markdown
[Greene and Cunningham. "Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/greene2006icml-practical/) doi:10.1145/1143844.1143892BibTeX
@inproceedings{greene2006icml-practical,
title = {{Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering}},
author = {Greene, Derek and Cunningham, Padraig},
booktitle = {International Conference on Machine Learning},
year = {2006},
pages = {377-384},
doi = {10.1145/1143844.1143892},
url = {https://mlanthology.org/icml/2006/greene2006icml-practical/}
}