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Title: A Robust Convex Formulations for Ensemble Clustering.
Authors: Gao, Junning
Yamada, Makoto  kyouindb  KAKEN_id
Kaski, Samuel
Mamitsuka, Hiroshi  kyouindb  KAKEN_id  orcid (unconfirmed)
Zhu, Shanfeng
Kambhampati, Subbarao
Author's alias: 馬見塚, 拓
Issue Date: Jul-2016
Publisher: AAAI Press・International Joint Conferences on Artificial Intelligence
Journal title: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016)
Start page: 1476
End page: 1482
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: International Joint Conference on Artificial Intelligence , New York City , United States , 9-16 July .
Rights: This is not the published version. Please cite only the published version.
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