A hybrid nonlinear-discriminant analysis feature projection technique

Khushaba, RN; Al-Ani, A; Al-Jumaily, A; Nguyen, HT

A hybrid nonlinear-discriminant analysis feature projection technique

Khushaba, RN

Al-Ani, A

Al-Jumaily, A

Nguyen, HT

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Publication Type:: Chapter
Citation:: 2008, 5360 LNAI pp. 544 - 550
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	2008, 5360 LNAI pp. 544 - 550	en_US
dc.identifier.isbn	3540893776	en_US
dc.identifier.isbn	9783540893776	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8108
dc.description.abstract	Feature set dimensionality reduction via Discriminant Analysis (DA) is one of the most sought after approaches in many applications. In this paper, a novel nonlinear DA technique is presented based on a hybrid of Artificial Neural Networks (ANN) and the Uncorrelated Linear Discriminant Analysis (ULDA). Although dimensionality reduction via ULDA can present a set of statistically uncorrelated features, but similar to the existing DA's it assumes that the original data set is linearly separable, which is not the case with most real world problems. In order to overcome this problem, a one layer feed-forward ANN trained with a Differential Evolution (DE) optimization technique is combined with ULDA to implement a nonlinear feature projection technique. This combination acts as nonlinear discriminant analysis. The proposed approach is validated on a Brain Computer Interface (BCI) problem and compared with other techniques. © 2008 Springer Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-540-89378-3_55	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A hybrid nonlinear-discriminant analysis feature projection technique	en_US
dc.type	Chapter
utslib.citation.volume	5360 LNAI	en_US
utslib.for	170205 Neurocognitive Patterns and Neural Networks	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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	5360 LNAI	en_US

Abstract:

Feature set dimensionality reduction via Discriminant Analysis (DA) is one of the most sought after approaches in many applications. In this paper, a novel nonlinear DA technique is presented based on a hybrid of Artificial Neural Networks (ANN) and the Uncorrelated Linear Discriminant Analysis (ULDA). Although dimensionality reduction via ULDA can present a set of statistically uncorrelated features, but similar to the existing DA's it assumes that the original data set is linearly separable, which is not the case with most real world problems. In order to overcome this problem, a one layer feed-forward ANN trained with a Differential Evolution (DE) optimization technique is combined with ULDA to implement a nonlinear feature projection technique. This combination acts as nonlinear discriminant analysis. The proposed approach is validated on a Brain Computer Interface (BCI) problem and compared with other techniques. © 2008 Springer Berlin Heidelberg.

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http://hdl.handle.net/10453/8108