Multiple kernel learning based multi-view spectral clustering

Guo, D; Zhang, J; Liu, X; Cui, Y; Zhao, C

Multiple kernel learning based multi-view spectral clustering

Guo, D Zhang, J

Liu, X Cui, Y Zhao, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2014, pp. 3774 - 3779
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Guo, D	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Cui, Y	en_US
dc.contributor.author	Zhao, C	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2014, pp. 3774 - 3779	en_US
dc.identifier.isbn	9781479952083	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121617
dc.description.abstract	© 2014 IEEE. For a given data set, exploring their multi-view instances under a clustering framework is a practical way to boost the clustering performance. This is because that each view might reflect partial information for the existing data. Furthermore, due to the noise and other impact factors, exploring these instances from different views will enhance the mining of the real structure and feature information within the data set. In this paper, we propose a multiple kernel spectral clustering algorithm through the multi-view instances on the given data set. By combining the kernel matrix learning and the spectral clustering optimization into one process framework, the algorithm can determine the kernel weights and cluster the multi-view data simultaneously. We compare the proposed algorithm with some recent published methods on real-world datasets to show the efficiency of the proposed algorithm.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/ICPR.2014.648	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Multiple kernel learning based multi-view spectral clustering	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. For a given data set, exploring their multi-view instances under a clustering framework is a practical way to boost the clustering performance. This is because that each view might reflect partial information for the existing data. Furthermore, due to the noise and other impact factors, exploring these instances from different views will enhance the mining of the real structure and feature information within the data set. In this paper, we propose a multiple kernel spectral clustering algorithm through the multi-view instances on the given data set. By combining the kernel matrix learning and the spectral clustering optimization into one process framework, the algorithm can determine the kernel weights and cluster the multi-view data simultaneously. We compare the proposed algorithm with some recent published methods on real-world datasets to show the efficiency of the proposed algorithm.

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http://hdl.handle.net/10453/121617