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Conference Paper: Robust Logistic Principal Component Regression for classification of data in presence of outliers
Title | Robust Logistic Principal Component Regression for classification of data in presence of outliers |
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Authors | |
Keywords | Classification of data High dimensional data Huber function M-estimation Microarray data |
Issue Date | 2012 |
Publisher | IEEE. 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? |
Abstract | The 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 Identifier | http://hdl.handle.net/10722/165245 |
ISBN | |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Wu, HC | en_US |
dc.contributor.author | Chan, SC | en_US |
dc.contributor.author | Tsui, KM | en_US |
dc.date.accessioned | 2012-09-20T08:16:30Z | - |
dc.date.available | 2012-09-20T08:16:30Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.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 | en_US |
dc.identifier.isbn | 978-1-4673-0219-7 | - |
dc.identifier.issn | 0271-4302 | - |
dc.identifier.uri | http://hdl.handle.net/10722/165245 | - |
dc.description.abstract | The 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.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089 | - |
dc.relation.ispartof | IEEE International Symposium on Circuits and Systems Proceedings | en_US |
dc.subject | Classification of data | - |
dc.subject | High dimensional data | - |
dc.subject | Huber function | - |
dc.subject | M-estimation | - |
dc.subject | Microarray data | - |
dc.title | Robust Logistic Principal Component Regression for classification of data in presence of outliers | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Wu, HC: andrewhcwu@eee.hku.hk | en_US |
dc.identifier.email | Chan, SC: ascchan@hkucc.hku.hk | en_US |
dc.identifier.email | Tsui, KM: kmtsui11@hku.hk | - |
dc.identifier.authority | Chan, SC=rp00094 | en_US |
dc.identifier.authority | Tsui, KM=rp00181 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ISCAS.2012.6271894 | - |
dc.identifier.scopus | eid_2-s2.0-84866613759 | - |
dc.identifier.hkuros | 208541 | en_US |
dc.identifier.spage | 2809 | - |
dc.identifier.epage | 2812 | - |
dc.publisher.place | United States | - |
dc.description.other | 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 | - |
dc.identifier.issnl | 0271-4302 | - |