Sample-adaptive multiple kernel learning

Liu, X; Wang, L; Zhang, J; Yin, J

Sample-adaptive multiple kernel learning

Liu, X Wang, L Zhang, J

Yin, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2014, 3 pp. 1975 - 1981
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Wang, L	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Yin, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2014, 3 pp. 1975 - 1981	en_US
dc.identifier.isbn	9781577356790	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121616
dc.description.abstract	Copyright © 2014, Association for the Advancement of Artificial Intelligence. Existing multiple kernel learning (MKL) algorithms indiscriminately apply a same set of kernel combination weights to all samples. However, the utility of base kernels could vary across samples and a base kernel useful for one sample could become noisy for another. In this case, rigidly applying a same set of kernel combination weights could adversely affect the learning performance. To improve this situation, we propose a sample-adaptive MKL algorithm, in which base kernels are allowed to be adaptively switched on/off with respect to each sample. We achieve this goal by assigning a latent binary variable to each base kernel when it is applied to a sample. The kernel combination weights and the iatent variables are jointly optimized via margin maximization principle. As demonstrated on five benchmark data sets, the proposed algorithm consistently outperforms the comparable ones in the literature.	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Sample-adaptive multiple kernel learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3	en_US
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	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

Copyright © 2014, Association for the Advancement of Artificial Intelligence. Existing multiple kernel learning (MKL) algorithms indiscriminately apply a same set of kernel combination weights to all samples. However, the utility of base kernels could vary across samples and a base kernel useful for one sample could become noisy for another. In this case, rigidly applying a same set of kernel combination weights could adversely affect the learning performance. To improve this situation, we propose a sample-adaptive MKL algorithm, in which base kernels are allowed to be adaptively switched on/off with respect to each sample. We achieve this goal by assigning a latent binary variable to each base kernel when it is applied to a sample. The kernel combination weights and the iatent variables are jointly optimized via margin maximization principle. As demonstrated on five benchmark data sets, the proposed algorithm consistently outperforms the comparable ones in the literature.

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http://hdl.handle.net/10453/121616