A Robust Convex Formulation for Ensemble Clustering
A4 Artikkeli konferenssijulkaisussa
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
AbstractWe 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.
ensemble clustering, convex optimization
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 >