A hybrid nonlinear-discriminant analysis feature projection technique

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dc.contributor.author Khushaba, RN
dc.contributor.author Al-Ani, A
dc.contributor.author Al-Jumaily, A
dc.contributor.author Nguyen, HT
dc.date.accessioned 2010-05-28T09:40:40Z
dc.date.issued 2008
dc.identifier.citation 2008, 5360 LNAI pp. 544 - 550
dc.identifier.isbn 3540893776
dc.identifier.isbn 9783540893776
dc.identifier.other B1 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.
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1007/978-3-540-89378-3_55
dc.title A hybrid nonlinear-discriminant analysis feature projection technique
dc.type Chapter
dc.journal.volume 5360 LNAI
dc.journal.number en_US
dc.publocation Germany en_US
dc.identifier.startpage 544 en_US
dc.identifier.endpage 550 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 170205 Neurocognitive Patterns and Neural Networks
dc.personcode 840115
dc.personcode 011083
dc.personcode 040052
dc.personcode 101188
dc.percentage 100 en_US
dc.classification.name Neurocognitive Patterns and Neural Networks en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Feature projection
dc.description.keywords Nonlinear discriminant analysis
pubs.embargo.period Not known
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 Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
pubs.consider-herdc true
utslib.collection.history General (ID: 2)


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