Robust Polynomial Classifier Using L1-norm minimization
In this paper we present a robust polynomial classifier based on L1-norm minimization. We do so by reformulating the classifier training process as a linear programming problem. Due to the inherent insensitivity of the L1-norm to influential observations, class models obtained via L1-norm minimizati...
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| Format: | article |
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2010
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| Online Access: | http://hdl.handle.net/11073/8833 |
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| _version_ | 1864513432452071424 |
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| author | Assaleh, Khaled |
| author2 | Shanableh, Tamer |
| author2_role | author |
| author_facet | Assaleh, Khaled Shanableh, Tamer |
| author_role | author |
| dc.creator.none.fl_str_mv | Assaleh, Khaled Shanableh, Tamer |
| dc.date.none.fl_str_mv | 2010 2017-05-04T11:10:26Z 2017-05-04T11:10:26Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Assaleh, K. & Shanableh, T. (2010). Robust polynomial classifier using L1-norm minimization. Applied Intelligence, 33(3), 330-339. doi:10.1007/s10489-009-0169-8 1573-7497 http://hdl.handle.net/11073/8833 10.1007/s10489-009-0169-8 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Springer |
| dc.relation.none.fl_str_mv | http://dx.doi.org/10.1007/s10489-009-0169-8 |
| dc.subject.none.fl_str_mv | Polynomial classifier Multivariate regression Pattern classification |
| dc.title.none.fl_str_mv | Robust Polynomial Classifier Using L1-norm minimization |
| dc.type.none.fl_str_mv | Postprint Peer-Reviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | In this paper we present a robust polynomial classifier based on L1-norm minimization. We do so by reformulating the classifier training process as a linear programming problem. Due to the inherent insensitivity of the L1-norm to influential observations, class models obtained via L1-norm minimization are much more robust than their counterparts obtained by the classical least squares minimization (L2-norm). For validation purposes, we apply this method to two recognition problems: character recognition and sign language recognition. Both are examined under different signal to noise ratio (SNR) values of the test data. Results show that L1-norm minimization provides superior recognition rates over L2-norm minimization when the training data contains influential observations especially if the test dataset is noisy. |
| format | article |
| id | aus_c98228e6869eda94efcee31db4b8674e |
| identifier_str_mv | Assaleh, K. & Shanableh, T. (2010). Robust polynomial classifier using L1-norm minimization. Applied Intelligence, 33(3), 330-339. doi:10.1007/s10489-009-0169-8 1573-7497 10.1007/s10489-009-0169-8 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8833 |
| publishDate | 2010 |
| publisher.none.fl_str_mv | Springer |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
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| spelling | Robust Polynomial Classifier Using L1-norm minimizationAssaleh, KhaledShanableh, TamerPolynomial classifierMultivariate regressionPattern classificationIn this paper we present a robust polynomial classifier based on L1-norm minimization. We do so by reformulating the classifier training process as a linear programming problem. Due to the inherent insensitivity of the L1-norm to influential observations, class models obtained via L1-norm minimization are much more robust than their counterparts obtained by the classical least squares minimization (L2-norm). For validation purposes, we apply this method to two recognition problems: character recognition and sign language recognition. Both are examined under different signal to noise ratio (SNR) values of the test data. Results show that L1-norm minimization provides superior recognition rates over L2-norm minimization when the training data contains influential observations especially if the test dataset is noisy.Springer2017-05-04T11:10:26Z2017-05-04T11:10:26Z2010PostprintPeer-Reviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAssaleh, K. & Shanableh, T. (2010). Robust polynomial classifier using L1-norm minimization. Applied Intelligence, 33(3), 330-339. doi:10.1007/s10489-009-0169-81573-7497http://hdl.handle.net/11073/883310.1007/s10489-009-0169-8en_UShttp://dx.doi.org/10.1007/s10489-009-0169-8oai:repository.aus.edu:11073/88332024-08-22T12:08:06Z |
| spellingShingle | Robust Polynomial Classifier Using L1-norm minimization Assaleh, Khaled Polynomial classifier Multivariate regression Pattern classification |
| status_str | publishedVersion |
| title | Robust Polynomial Classifier Using L1-norm minimization |
| title_full | Robust Polynomial Classifier Using L1-norm minimization |
| title_fullStr | Robust Polynomial Classifier Using L1-norm minimization |
| title_full_unstemmed | Robust Polynomial Classifier Using L1-norm minimization |
| title_short | Robust Polynomial Classifier Using L1-norm minimization |
| title_sort | Robust Polynomial Classifier Using L1-norm minimization |
| topic | Polynomial classifier Multivariate regression Pattern classification |
| url | http://hdl.handle.net/11073/8833 |