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dc.contributor.authorLuo, Xianghuien_NZ
dc.contributor.authorDurrant, Robert J.en_NZ
dc.coverage.spatialBeijing, Chinaen_NZ
dc.date.accessioned2019-11-14T21:56:40Z
dc.date.available2018-01-01en_NZ
dc.date.available2019-11-14T21:56:40Z
dc.date.issued2018en_NZ
dc.identifier.citationLuo, X., & Durrant, R. J. (2018). Maximum gradient dimensionality reduction. In Proceedings of 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 501–506). Washington, DC, USA: IEEE. https://doi.org/10.1109/ICPR.2018.8546198en
dc.identifier.issn1051-4651en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/13139
dc.description.abstractWe propose a novel dimensionality reduction approach based on the gradient of the regression function. Our approach is conceptually similar to Principal Component Analysis, however instead of seeking a low dimensional representation of the predictors that preserve the sample variance, we project onto a basis that preserves those predictors which induce the greatest change in the response. Our approach has the benefits of being simple and easy to implement and interpret, while still remaining very competitive with sophisticated state-of-the-art approaches.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen_NZ
dc.rightsThis is an author’s accepted version of an article published in the Proceedings of 2018 24th International Conference on Pattern Recognition (ICPR). © 2018 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.source24th International Conference on Pattern Recognition (ICPR)en_NZ
dc.subjectScience & Technologyen_NZ
dc.subjectTechnologyen_NZ
dc.subjectComputer Science, Artificial Intelligenceen_NZ
dc.subjectComputer Scienceen_NZ
dc.subjectPREDICTIONen_NZ
dc.subjectREGRESSIONen_NZ
dc.subjectDISCOVERYen_NZ
dc.subjectCANCERen_NZ
dc.subjectMachine learning
dc.titleMaximum gradient dimensionality reductionen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1109/ICPR.2018.8546198
dc.relation.isPartOfProceedings of 2018 24th International Conference on Pattern Recognition (ICPR)en_NZ
pubs.begin-page501
pubs.elements-id233895
pubs.end-page506
pubs.finish-date2018-08-24en_NZ
pubs.place-of-publicationWashington, DC, USA
pubs.publication-statusPublisheden_NZ
pubs.start-date2018-08-20en_NZ


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