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Conference Paper: Signal self organizing map

TitleSignal self organizing map
Authors
Issue Date2007
PublisherInstitute of Electrical and Electronics Engineers. The Journals web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500
Citation
The 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL., 12-17 August 2007. In IEEE International Conference on Neural Networks Proceedings, 2007, p. 213-218 How to Cite?
AbstractThe self organizing map (SOM) has been applied to wide ranges of fields including computer vision and image processing. Despite of its simple training algorithm, the vectoral input pattern of SOMs induced a sequence of drawbacks which should not be overlooked. These drawbacks include optimal description length selection problem and inaccurate clustering of scattered point patterns. In this article, an extension of SOM to continuous domain, namely signal SOM (SSOM), is proposed to tackle the drawbacks caused by the vectoral input pattern SOMs. Remarkably, it provides an analytical model expression and involves no model selection problem. The SSOM is evaluated by a simulation about clustering of three signal groups. By comparing with the conventional SOM, a more structural map in term of signal group distribution is obtained by the SSOM. Thus, it indicate the contribution of this article on extending the ability of SOM. ©2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/196697
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChow, CK-
dc.contributor.authorYuen, SY-
dc.date.accessioned2014-04-24T02:10:34Z-
dc.date.available2014-04-24T02:10:34Z-
dc.date.issued2007-
dc.identifier.citationThe 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL., 12-17 August 2007. In IEEE International Conference on Neural Networks Proceedings, 2007, p. 213-218-
dc.identifier.isbn978-1-4244-1379-9-
dc.identifier.issn1098-7576-
dc.identifier.urihttp://hdl.handle.net/10722/196697-
dc.description.abstractThe self organizing map (SOM) has been applied to wide ranges of fields including computer vision and image processing. Despite of its simple training algorithm, the vectoral input pattern of SOMs induced a sequence of drawbacks which should not be overlooked. These drawbacks include optimal description length selection problem and inaccurate clustering of scattered point patterns. In this article, an extension of SOM to continuous domain, namely signal SOM (SSOM), is proposed to tackle the drawbacks caused by the vectoral input pattern SOMs. Remarkably, it provides an analytical model expression and involves no model selection problem. The SSOM is evaluated by a simulation about clustering of three signal groups. By comparing with the conventional SOM, a more structural map in term of signal group distribution is obtained by the SSOM. Thus, it indicate the contribution of this article on extending the ability of SOM. ©2007 IEEE.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journals web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000500-
dc.relation.ispartofIEEE International Conference on Neural Networks Proceedings-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE International Conference on Neural Networks Proceedings. Copyright © Institute of Electrical and Electronics Engineers.-
dc.titleSignal self organizing map-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/IJCNN.2007.4370957-
dc.identifier.scopuseid_2-s2.0-51749091985-
dc.identifier.spage213-
dc.identifier.epage218-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160603 amended-

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