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Conference Paper: The role of Eigen-matrix translation in classification of biological datasets

TitleThe role of Eigen-matrix translation in classification of biological datasets
Authors
KeywordsClassification
Dimension reduction
Eigen-matrix translation
Kernel method (KM)
Support vector machine (SVM)
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001586
Citation
The 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012), Philadelphia, U.S., 4-7 October 2012. In IEEE BIBM Proceedings, 2012, p. 373-376 How to Cite?
AbstractDriven by the challenge of integrating large amount of experimental data obtained from biological research, computational biology and bioinformatics are growing rapidly. Machine learning methods, especially kernel methods with Support Vector Machines (SVMs) are very popular tools. In the perspective of kernel matrix, a technique namely Eigen-matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy owns a lot of nice properties while the nature of which needs further exploration. We propose that its importance lies in the dimension reduction of predictor attributes within the data set. This can therefore serve as a novel perspective for future research in dimension reduction problems. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/181780
ISBN

 

DC FieldValueLanguage
dc.contributor.authorJiang, Hen_US
dc.contributor.authorChing, WKen_US
dc.date.accessioned2013-03-19T03:57:21Z-
dc.date.available2013-03-19T03:57:21Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012), Philadelphia, U.S., 4-7 October 2012. In IEEE BIBM Proceedings, 2012, p. 373-376en_US
dc.identifier.isbn978-1-4673-2560-8-
dc.identifier.urihttp://hdl.handle.net/10722/181780-
dc.description.abstractDriven by the challenge of integrating large amount of experimental data obtained from biological research, computational biology and bioinformatics are growing rapidly. Machine learning methods, especially kernel methods with Support Vector Machines (SVMs) are very popular tools. In the perspective of kernel matrix, a technique namely Eigen-matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy owns a lot of nice properties while the nature of which needs further exploration. We propose that its importance lies in the dimension reduction of predictor attributes within the data set. This can therefore serve as a novel perspective for future research in dimension reduction problems. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001586-
dc.relation.ispartofIEEE International Conference on Bioinformatics and Biomedicine Proceedingsen_US
dc.rightsIEEE International Conference on Bioinformatics and Biomedicine Proceedings. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectClassification-
dc.subjectDimension reduction-
dc.subjectEigen-matrix translation-
dc.subjectKernel method (KM)-
dc.subjectSupport vector machine (SVM)-
dc.titleThe role of Eigen-matrix translation in classification of biological datasetsen_US
dc.typeConference_Paperen_US
dc.identifier.emailJiang, H: haohao@hkusuc.hku.hken_US
dc.identifier.emailChing, WK: wching@hku.hk-
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/BIBM.2012.6392701-
dc.identifier.scopuseid_2-s2.0-84872532337-
dc.identifier.hkuros213618en_US
dc.identifier.spage373-
dc.identifier.epage376-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130409-

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