---
title: "Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization"
authors:
  - "Peng Zhou"
  - "Liang Du"
  - "Hanmo Wang"
  - "Lei Shi"
  - "Yi-Dong Shen"
doi: ""
url: "https://ijcai.org/proceedings/2015/577"
pdf: "https://ijcai.org/proceedings/2015/0577.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "4112-4117"
track: "Special Track on Machine Learning"
keywords:
---

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

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

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, 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.