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Conference Paper: Weighted local least squares imputation method for missing value estimation

TitleWeighted local least squares imputation method for missing value estimation
Authors
KeywordsMissing values
Microarray data
Row average method
Local least squares imputation method
Weighted local least squares imputation method
Issue Date2007
PublisherWorld Publishing Corporation
Citation
The International Symposium on Optimization and Systems Biology (OSB 2007), Beijing, China, 8-10 August 2007. In Zhang, XS, Chen, L, and Wu, LY et al. (Eds.). Lecture Notes in Operations Research 7, p.280-287. Beijing, China: World Publishing Corporation, 2007 How to Cite?
AbstractMissing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imputation (LLSI) method for estimating the missing values. In this paper, we propose a Weighted Local Least Square Imputation (WLLSI) method for missing values estimation. WLLSI allows training on the weighting and therefore can take advantage of both the LLSI method and the RA method. Numerical results on both synthetic data and real microarray data are given to demonstrate the effectiveness of our proposed method. The imputation methods are then applied to a breast cancer dataset.
Persistent Identifierhttp://hdl.handle.net/10722/100380
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorCheng, KWen_HK
dc.contributor.authorLi, Len_HK
dc.contributor.authorTsing, NKen_HK
dc.contributor.authorWong, ASTen_HK
dc.date.accessioned2010-09-25T19:07:41Z-
dc.date.available2010-09-25T19:07:41Z-
dc.date.issued2007en_HK
dc.identifier.citationThe International Symposium on Optimization and Systems Biology (OSB 2007), Beijing, China, 8-10 August 2007. In Zhang, XS, Chen, L, and Wu, LY et al. (Eds.). Lecture Notes in Operations Research 7, p.280-287. Beijing, China: World Publishing Corporation, 2007-
dc.identifier.isbn978-7-5062-7292-6/O568-
dc.identifier.urihttp://hdl.handle.net/10722/100380-
dc.description.abstractMissing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imputation (LLSI) method for estimating the missing values. In this paper, we propose a Weighted Local Least Square Imputation (WLLSI) method for missing values estimation. WLLSI allows training on the weighting and therefore can take advantage of both the LLSI method and the RA method. Numerical results on both synthetic data and real microarray data are given to demonstrate the effectiveness of our proposed method. The imputation methods are then applied to a breast cancer dataset.-
dc.languageengen_HK
dc.publisherWorld Publishing Corporation-
dc.relation.ispartofLecture Notes in Operations Research 7en_HK
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectMissing values-
dc.subjectMicroarray data-
dc.subjectRow average method-
dc.subjectLocal least squares imputation method-
dc.subjectWeighted local least squares imputation method-
dc.titleWeighted local least squares imputation method for missing value estimationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChing, WK: wching@HKUCC.hku.hken_HK
dc.identifier.emailTsing, NK: nktsing@hku.hken_HK
dc.identifier.emailWong, AST: awong1@hkucc.hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.identifier.authorityTsing, NK=rp00794en_HK
dc.identifier.authorityWong, AST=rp00805en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros130213en_HK

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