Accelerated training algorithms of general fuzzy min-max neural network using GPU for very high dimensional data

Khuat, TT; Gabrys, B

Accelerated training algorithms of general fuzzy min-max neural network using GPU for very high dimensional data

Khuat, TT

Gabrys, B

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11953 LNCS pp. 583 - 595
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Khuat, TT https://orcid.org/0000-0002-6456-8530	en_US
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11953 LNCS pp. 583 - 595	en_US
dc.identifier.isbn	9783030367077	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137412
dc.description.abstract	© Springer Nature Switzerland AG 2019. One of the issues of training a general fuzzy min-max neural network (GFMM) on very high dimensional data is a long training time even if the number of samples is relatively low. This is a quite common problem shared by many prototype-based methods requiring frequently repeated distance or similarity calculations. This paper proposes the method of accelerating the learning algorithms of the GFMM by, first, reformulating and representing them in a format allowing for their parallel execution and subsequently leveraging the computational power of the graphics processing unit (GPU). The original implementation of GFMM is modified by matrix computations to be executed on the GPU for the very high-dimensional datasets. The empirical results on two very high-dimensional datasets indicated that the training and testing processes performed on Nvidia Quadro P5000 GPU were from 10 to 35 times faster compared to those running serially on the Xeon CPU while retaining the same classification accuracy.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-36708-4_48	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Accelerated training algorithms of general fuzzy min-max neural network using GPU for very high dimensional data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11953 LNCS	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
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	11953 LNCS	en_US

Abstract:

© Springer Nature Switzerland AG 2019. One of the issues of training a general fuzzy min-max neural network (GFMM) on very high dimensional data is a long training time even if the number of samples is relatively low. This is a quite common problem shared by many prototype-based methods requiring frequently repeated distance or similarity calculations. This paper proposes the method of accelerating the learning algorithms of the GFMM by, first, reformulating and representing them in a format allowing for their parallel execution and subsequently leveraging the computational power of the graphics processing unit (GPU). The original implementation of GFMM is modified by matrix computations to be executed on the GPU for the very high-dimensional datasets. The empirical results on two very high-dimensional datasets indicated that the training and testing processes performed on Nvidia Quadro P5000 GPU were from 10 to 35 times faster compared to those running serially on the Xeon CPU while retaining the same classification accuracy.

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http://hdl.handle.net/10453/137412