Proceedings

A Robust Convex Formulation for Ensemble Clustering

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

Download BibTeX Cite

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1476-1481.

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.

Junning Gao, Makoto Yamada, Samuel Kaski, Hiroshi Mamitsuka, and Shanfeng Zhu. 2016. A Robust Convex Formulation for Ensemble Clustering. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 1476–1481. https://ijcai.org/proceedings/2016/212
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards