Proceedings

Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization

Peng Zhou, Liang Du, Hanmo Wang, Lei Shi, Yi-Dong Shen

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 4112-4117.

Abstract

Clustering ensemble has emerged as an important extension of the classical clustering problem. It provides a framework for combining multiple base clusterings of a data set to generate a final consensus result. Most existing clustering methods simply combine clustering results without taking into account the noises, which may degrade the clustering performance. In this paper, we propose a novel robust clustering ensemble method. To improve the robustness, we capture the sparse and symmetric errors and integrate them into our robust and consensus framework to learn a low-rank matrix. Since the optimization of the objective function is difficult to solve, we develop a block coordinate descent algorithm which is theoretically guaranteed to converge. Experimental results on real world data sets demonstrate the effectiveness of our method.

Peng Zhou, Liang Du, Hanmo Wang, Lei Shi, and Yi-Dong Shen. 2015. Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 4112–4117. https://ijcai.org/proceedings/2015/577
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