AAAI Publications, Twenty-Fourth International Joint Conference on Artificial Intelligence

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Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization
Peng Zhou, Liang Du, Hanmo Wang, Lei Shi, Yi-Dong Shen

Last modified: 2015-06-27

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.

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