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- Publisher Website: 10.1109/BIBM.2012.6392701
- Scopus: eid_2-s2.0-84872532337
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Conference Paper: The role of Eigen-matrix translation in classification of biological datasets
Title | The role of Eigen-matrix translation in classification of biological datasets |
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Authors | |
Keywords | Classification Dimension reduction Eigen-matrix translation Kernel method (KM) Support vector machine (SVM) |
Issue Date | 2012 |
Publisher | IEEE. 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? |
Abstract | Driven 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 Identifier | http://hdl.handle.net/10722/181780 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Jiang, H | en_US |
dc.contributor.author | Ching, WK | en_US |
dc.date.accessioned | 2013-03-19T03:57:21Z | - |
dc.date.available | 2013-03-19T03:57:21Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.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 | en_US |
dc.identifier.isbn | 978-1-4673-2560-8 | - |
dc.identifier.uri | http://hdl.handle.net/10722/181780 | - |
dc.description.abstract | Driven 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.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001586 | - |
dc.relation.ispartof | IEEE International Conference on Bioinformatics and Biomedicine Proceedings | en_US |
dc.subject | Classification | - |
dc.subject | Dimension reduction | - |
dc.subject | Eigen-matrix translation | - |
dc.subject | Kernel method (KM) | - |
dc.subject | Support vector machine (SVM) | - |
dc.title | The role of Eigen-matrix translation in classification of biological datasets | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Jiang, H: haohao@hkusuc.hku.hk | en_US |
dc.identifier.email | Ching, WK: wching@hku.hk | - |
dc.identifier.authority | Ching, WK=rp00679 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/BIBM.2012.6392701 | - |
dc.identifier.scopus | eid_2-s2.0-84872532337 | - |
dc.identifier.hkuros | 213618 | en_US |
dc.identifier.spage | 373 | - |
dc.identifier.epage | 376 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 130409 | - |