A Robust Convex Formulation for Ensemble Clustering

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

A Robust Convex Formulation for Ensemble Clustering

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

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

137 Downloads (Pure)

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.

Original language	English
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
Abbreviated title	IJCAI
Country/Territory	United States
City	New York
Period	09/07/2016 → 15/07/2016
Internet address	http://ijcai-16.org/index.php/welcome/view/home

Keywords

ensemble clustering
convex optimization

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http://www.ijcai.org/Proceedings/16/Papers/212.pdf

Interactive machine learning from multiple biodata sources
Kaski, S. (Principal investigator)
01/01/2016 → 31/08/2021
Project: Academy of Finland: Other research funding
Interactive machine learning from multiple biodata sources
Kaski, S. (Principal investigator)
01/01/2016 → 31/12/2018
Project: Academy of Finland: Other research funding

Cite this

@inproceedings{90901dfe0a204f238bdf1a89e1b054b2,

title = "A Robust Convex Formulation for Ensemble Clustering",

abstract = "We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimizationalgorithm, which we call Robust Convex Ensemble Clustering (RCEC). A key feature of RCEC allows to remove anomalous cluster assignments obtainedfrom 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.",

keywords = "ensemble clustering, convex optimization",

author = "Junning Gao and Makoto Yamada and Samuel Kaski and Hiroshi Mamitsuka and Shanfeng Zhu",

year = "2016",

month = jul,

language = "English",

isbn = "978-1-57735-770-4",

pages = "1476--1482",

editor = "Subbarao Kambhampati",

booktitle = "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)",

publisher = "AAAI Press",

address = "United States",

note = "International Joint Conference on Artificial Intelligence, IJCAI ; Conference date: 09-07-2016 Through 15-07-2016",

url = "http://ijcai-16.org/index.php/welcome/view/home",

}

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>

A Robust Convex Formulation for Ensemble Clustering. / Gao, Junning; Yamada, Makoto; Kaski, Samuel et al.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). ed. / Subbarao Kambhampati. 2275 East Bayshore Road, Suite 160, Palo Alto CA 94303 USA: AAAI Press, 2016. p. 1476-1482.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - A Robust Convex Formulation for Ensemble Clustering

AU - Gao, Junning

AU - Yamada, Makoto

AU - Kaski, Samuel

AU - Mamitsuka, Hiroshi

AU - Zhu, Shanfeng

N1 - Conference code: 25

PY - 2016/7

Y1 - 2016/7

N2 - We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimizationalgorithm, which we call Robust Convex Ensemble Clustering (RCEC). A key feature of RCEC allows to remove anomalous cluster assignments obtainedfrom 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.

AB - We formulate ensemble clustering as a regularization problem over nuclear norm and cluster-wise group norm, and present an efficient optimizationalgorithm, which we call Robust Convex Ensemble Clustering (RCEC). A key feature of RCEC allows to remove anomalous cluster assignments obtainedfrom 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.

KW - ensemble clustering

KW - convex optimization

M3 - Conference article in proceedings

SN - 978-1-57735-770-4

SP - 1476

EP - 1482

BT - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)

A2 - Kambhampati, Subbarao

PB - AAAI Press

CY - 2275 East Bayshore Road, Suite 160, Palo Alto CA 94303 USA

T2 - International Joint Conference on Artificial Intelligence

Y2 - 9 July 2016 through 15 July 2016

ER -

A Robust Convex Formulation for Ensemble Clustering

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Interactive machine learning from multiple biodata sources

Interactive machine learning from multiple biodata sources

Cite this