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dc.contributor.authorGan, Xiangchao
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorYan, Hong
dc.contributor.editorTang, YY
dc.contributor.editorWang, SP
dc.contributor.editorLorette, G
dc.contributor.editorYeung, DS
dc.contributor.editorYan, H
dc.date.accessioned2017-05-03T15:20:33Z
dc.date.available2017-05-03T15:20:33Z
dc.date.issued2006
dc.date.modified2010-10-27T08:28:51Z
dc.identifier.isbn9780769525211
dc.identifier.issn1051-4651
dc.identifier.doi10.1109/ICPR.2006.796
dc.identifier.urihttp://hdl.handle.net/10072/24404
dc.description.abstractGene expressions measured using microarrays usually suffer from the missing value problem. Existing missing value imputation algorithms have some limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by a global structure. In addition, these algorithms do not take into account many biological constraints in the imputation procedure. In this paper, we propose a set theoretic framework for missing data imputation. We design our algorithm by taking into consideration the biological characteristic of the data and exploit the local correlation and the global correlation structure adaptively. Experiments show that our algorithm can achieve a significant reduction of error compared with existing methods.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent141846 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE Computer Society
dc.publisher.placeUSA
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename18th International Conference on Pattern Recognition (ICPR 2006)
dc.relation.ispartofconferencetitle18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS
dc.relation.ispartofdatefrom2006-08-20
dc.relation.ispartofdateto2006-08-24
dc.relation.ispartoflocationHong Kong, PEOPLES R CHINA
dc.relation.ispartofpagefrom842
dc.relation.ispartofpageto+
dc.relation.ispartofvolume3
dc.rights.retentionY
dc.subject.fieldofresearchcode270201
dc.subject.fieldofresearchcode280204
dc.titleMicroarray Missing Data Imputation based on a Set Theoretic Framework and Biological Constraints
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.rights.copyright© 2006 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.
gro.date.issued2006
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


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