Two-stage Unsupervised Multiple Kernel Extreme Learning Machine

Zhao, G; Xiang, L; Zhu, C; Li, F

Two-stage Unsupervised Multiple Kernel Extreme Learning Machine

Zhao, G Xiang, L Zhu, C

Li, F

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2018 International Joint Conference on Neural Networks (IJCNN), 2018
Issue Date:: 2018-07

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Field	Value	Language
dc.contributor.author	Zhao, G	en_US
dc.contributor.author	Xiang, L	en_US
dc.contributor.author	Zhu, C https://orcid.org/0000-0003-0495-1981	en_US
dc.contributor.author	Li, F	en_US
dc.date	2018-07-08	en_US
dc.date.issued	2018-07	en_US
dc.identifier.citation	2018 International Joint Conference on Neural Networks (IJCNN), 2018	en_US
dc.identifier.isbn	978-1-5090-6014-6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128698
dc.description.abstract	As a powerful learning tool, Extreme Learning Machine (ELM) shows its merits in classification, regression and clustering by offering both high prediction accuracy and high learning speed. Among numerous ELM varieties, multiple kernel ELM draws intensive attention from researchers because it can leverage information from multiple heterogeneous sources, which is a common scenario in big data era. Despite remarkable efforts for supervised multiple kernel ELM, few publications have addressed the unsupervised case, which is more critical yet challenging for tackling realistic problem. In this paper, we address this problem by proposing a two-stage unsupervised multiple kernel extreme learning machine, which is suitable for fast multiple-view clustering. This approach learns the cluster and kernel combination weights alternatively. At the first stage, it generates cluster label based on a given combined kernel. Then, at the second stage, the kernel combination weights are learned by distance label based extreme learning machine based on the label generated at the previous stage. Experimental results on both synthetic and real data sets demonstrate its outstanding performance in term of both accuracy and learning speed.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	2018 International Joint Conference on Neural Networks (IJCNN)	en_US
dc.relation.ispartof	International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/ijcnn.2018.8489529	en_US
dc.rights	© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Two-stage Unsupervised Multiple Kernel Extreme Learning Machine	en_US
dc.type	Conference Proceeding
utslib.location.activity	Rio, Brazil	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2018-07-13	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2018-07-08	en_US

Abstract:

As a powerful learning tool, Extreme Learning Machine (ELM) shows its merits in classification, regression and clustering by offering both high prediction accuracy and high learning speed. Among numerous ELM varieties, multiple kernel ELM draws intensive attention from researchers because it can leverage information from multiple heterogeneous sources, which is a common scenario in big data era. Despite remarkable efforts for supervised multiple kernel ELM, few publications have addressed the unsupervised case, which is more critical yet challenging for tackling realistic problem. In this paper, we address this problem by proposing a two-stage unsupervised multiple kernel extreme learning machine, which is suitable for fast multiple-view clustering. This approach learns the cluster and kernel combination weights alternatively. At the first stage, it generates cluster label based on a given combined kernel. Then, at the second stage, the kernel combination weights are learned by distance label based extreme learning machine based on the label generated at the previous stage. Experimental results on both synthetic and real data sets demonstrate its outstanding performance in term of both accuracy and learning speed.

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http://hdl.handle.net/10453/128698