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Conference Paper: Robust Logistic Principal Component Regression for classification of data in presence of outliers

TitleRobust Logistic Principal Component Regression for classification of data in presence of outliers
Authors
KeywordsClassification of data
High dimensional data
Huber function
M-estimation
Microarray data
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089
Citation
The 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 2809-2812 How to Cite?
AbstractThe Logistic Principal Component Regression (LPCR) has found many applications in classification of high-dimensional data, such as tumor classification using microarray data. However, when the measurements are contaminated and/or the observations are mislabeled, the performance of the LPCR will be significantly degraded. In this paper, we propose a new robust LPCR based on M-estimation, which constitutes a versatile framework to reduce the sensitivity of the estimators to outliers. In particular, robust detection rules are used to first remove the contaminated measurements and then a modified Huber function is used to further remove the contributions of the mislabeled observations. Experimental results show that the proposed method generally outperforms the conventional LPCR under the presence of outliers, while maintaining a performance comparable to that obtained under normal condition. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/165245
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWu, HCen_US
dc.contributor.authorChan, SCen_US
dc.contributor.authorTsui, KMen_US
dc.date.accessioned2012-09-20T08:16:30Z-
dc.date.available2012-09-20T08:16:30Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 2809-2812en_US
dc.identifier.isbn978-1-4673-0219-7-
dc.identifier.issn0271-4302-
dc.identifier.urihttp://hdl.handle.net/10722/165245-
dc.description.abstractThe Logistic Principal Component Regression (LPCR) has found many applications in classification of high-dimensional data, such as tumor classification using microarray data. However, when the measurements are contaminated and/or the observations are mislabeled, the performance of the LPCR will be significantly degraded. In this paper, we propose a new robust LPCR based on M-estimation, which constitutes a versatile framework to reduce the sensitivity of the estimators to outliers. In particular, robust detection rules are used to first remove the contaminated measurements and then a modified Huber function is used to further remove the contributions of the mislabeled observations. Experimental results show that the proposed method generally outperforms the conventional LPCR under the presence of outliers, while maintaining a performance comparable to that obtained under normal condition. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089-
dc.relation.ispartofIEEE International Symposium on Circuits and Systems Proceedingsen_US
dc.rightsIEEE International Symposium on Circuits and Systems 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 of data-
dc.subjectHigh dimensional data-
dc.subjectHuber function-
dc.subjectM-estimation-
dc.subjectMicroarray data-
dc.titleRobust Logistic Principal Component Regression for classification of data in presence of outliersen_US
dc.typeConference_Paperen_US
dc.identifier.emailWu, HC: andrewhcwu@eee.hku.hken_US
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_US
dc.identifier.emailTsui, KM: kmtsui11@hku.hk-
dc.identifier.authorityChan, SC=rp00094en_US
dc.identifier.authorityTsui, KM=rp00181en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISCAS.2012.6271894-
dc.identifier.scopuseid_2-s2.0-84866613759-
dc.identifier.hkuros208541en_US
dc.identifier.spage2809-
dc.identifier.epage2812-
dc.publisher.placeUnited States-
dc.description.otherThe 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 2809-2812-

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