Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/69851
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dc.contributor.authorPaisitkriangkrai, S.en
dc.contributor.authorShen, C.en
dc.contributor.authorVan Den Hengel, A.en
dc.date.issued2012en
dc.identifier.citationProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), held in Rhode Island, USA, 16-21 June 2012: pp. 2128-2135en
dc.identifier.isbn9781467312264en
dc.identifier.issn1063-6919en
dc.identifier.urihttp://hdl.handle.net/2440/69851-
dc.description.abstractWe present a novel formulation of fully corrective boosting for multi-class classification problems with the awareness of sharing features. Our multi-class boosting is solved in a single optimization problem. In order to share features across different classes, we introduce the mixed-norm regularization, which promotes group sparsity, into boosting. We then derive the Lagrange dual problems which enable us to design fully corrective multi-class algorithms using the primal-dual optimization technique. We show that sharing features across classes can improve classification performance and efficiency. We empirically show that in many cases, the proposed multi-class boosting generalizes better than a range of competing multi-class boosting algorithms due to the capability of feature sharing. Experimental results on machine learning data, visual scene and object recognition demonstrate the efficiency and effectiveness of proposed algorithms and validate our theoretical findings.en
dc.description.statementofresponsibilitySakrapee Paisitkriangkrai, Chunhua Shen and Anton van den Hengelen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognitionen
dc.rightsCopyright IEEEen
dc.source.urihttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6235193en
dc.subjectboosting; multi-class classification; feature sharing; column generation; convex optimizationen
dc.titleSharing features in multi-class boosting via group sparsityen
dc.typeConference paperen
dc.identifier.rmid0020122223en
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (25th : 2012 : Providence, Rhode Island)en
dc.identifier.doi10.1109/CVPR.2012.6247919en
dc.publisher.placeUSAen
dc.identifier.pubid23007-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Computer Science publications

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