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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.
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 language | English |
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Title of host publication | Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) |
Editors | Subbarao Kambhampati |
Place of Publication | 2275 East Bayshore Road, Suite 160, Palo Alto CA 94303 USA |
Publisher | AAAI Press |
Pages | 1476-1482 |
Number of pages | 6 |
ISBN (Print) | 978-1-57735-770-4 |
Publication status | Published - Jul 2016 |
MoE publication type | A4 Conference publication |
Event | International Joint Conference on Artificial Intelligence - New York Hilton Midtown, New York, United States Duration: 9 Jul 2016 → 15 Jul 2016 Conference number: 25 http://ijcai-16.org/index.php/welcome/view/home |
Conference
Conference | International Joint Conference on Artificial Intelligence |
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Abbreviated title | IJCAI |
Country/Territory | United States |
City | New York |
Period | 09/07/2016 → 15/07/2016 |
Internet address |
Keywords
- ensemble clustering
- convex optimization
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Dive into the research topics of 'A Robust Convex Formulation for Ensemble Clustering'. Together they form a unique fingerprint.Projects
- 2 Finished
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Interactive machine learning from multiple biodata sources
Kaski, S. & Filstroff, L.
01/01/2016 → 31/08/2021
Project: Academy of Finland: Other research funding
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Interactive machine learning from multiple biodata sources
Sundin, I., Kaski, S., Afrabandpey, H., Chen, Y., Aushev, A., Honkamaa, J., Blomstedt, P., Hegde, P., Siren, J., Pesonen, H., Kangas, J., Qin, X., Shen, Z., Peltola, T., Celikok, M. M., Daee, P., Eranti, P., Jälkö, J. & Reinvall, J.
01/01/2016 → 31/12/2018
Project: Academy of Finland: Other research funding