A Robust Convex Formulation for Ensemble Clustering

Junning Gao, Makoto Yamada, Samuel Kaski, Hiroshi Mamitsuka, Shanfeng Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)
112 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
EditorsSubbarao Kambhampati
Place of Publication2275 East Bayshore Road, Suite 160, Palo Alto CA 94303 USA
PublisherAAAI Press
Number of pages6
ISBN (Print)978-1-57735-770-4
Publication statusPublished - Jul 2016
MoE publication typeA4 Conference publication
EventInternational Joint Conference on Artificial Intelligence - New York Hilton Midtown, New York, United States
Duration: 9 Jul 201615 Jul 2016
Conference number: 25


ConferenceInternational Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
Country/TerritoryUnited States
CityNew York
Internet address


  • ensemble clustering
  • convex optimization


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