{"type":"ScholarlyArticle","title":"Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization","authors":[{"name":"Peng Zhou","slug":"peng-zhou","given":"Peng","family":"Zhou","affiliation":""},{"name":"Liang Du","slug":"liang-du","given":"Liang","family":"Du","affiliation":""},{"name":"Hanmo Wang","slug":"hanmo-wang","given":"Hanmo","family":"Wang","affiliation":""},{"name":"Lei Shi","slug":"lei-shi","given":"Lei","family":"Shi","affiliation":""},{"name":"Yi-Dong Shen","slug":"yi-dong-shen","given":"Yi-Dong","family":"Shen","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2015/577","pdf":"https://ijcai.org/proceedings/2015/0577.pdf","markdown":"https://ijcai.org/proceedings/2015/577.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/577","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":577,"pages":{"first":4112,"last":4117},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}