A Robust Convex Formulation for Ensemble Clustering

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2016-07
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
1476-1482
Series
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
Abstract
We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimization algorithm, which we call Robust Convex Ensemble Clustering (RCEC). A key feature of RCEC allows to remove anomalous cluster assignments obtained from component clustering methods by using the group-norm regularization. Moreover, the proposed method is convex and can find the globally optimal solution. We first showed that using synthetic data experiments, RCEC could learn stable cluster assignments from the input matrix including anomalous clusters. We then showed that RCEC outperformed state-of-the-art ensemble clustering methods by using real-world data sets.
Description
Keywords
ensemble clustering, convex optimization
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Citation
Gao , J , Yamada , M , Kaski , S , Mamitsuka , H & Zhu , S 2016 , A Robust Convex Formulation for Ensemble Clustering . in S Kambhampati (ed.) , Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) . AAAI Press , 2275 East Bayshore Road, Suite 160, Palo Alto CA 94303 USA , pp. 1476-1482 , International Joint Conference on Artificial Intelligence , New York , New York , United States , 09/07/2016 . < http://www.ijcai.org/Proceedings/16/Papers/212.pdf >