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dc.contributor.convenorAndrew Bradleyen_US
dc.contributor.authorLi, Hanxien_US
dc.contributor.authorGao, Yongshengen_US
dc.contributor.authorSun, Junen_US
dc.contributor.editorIEEEen_US
dc.date.accessioned2017-05-03T15:01:12Z
dc.date.available2017-05-03T15:01:12Z
dc.date.issued2011en_US
dc.date.modified2012-02-20T05:51:30Z
dc.identifier.refurihttp://itee.uq.edu.au/~dicta2011/en_US
dc.identifier.doi10.1109/DICTA.2011.20en_US
dc.identifier.urihttp://hdl.handle.net/10072/42997
dc.description.abstractTwo efficient algorithms are proposed to seek the sparse representation on high-dimensional Hilbert space. By proving that all the calculations in Orthogonal Match Pursuit (OMP) are essentially inner-product combinations, we modify the OMP algorithm to apply the kernel-trick. The proposed Kernel OMP (KOMP) is much faster than the existing methods, and illustrates higher accuracy in some scenarios. Furthermore, inspired by the success of group-sparsity, we enforce a rigid group-sparsity constraint on KOMP which leads to a noniterative variation. The constrained cousin of KOMP, dubbed as Single-Step KOMP (S-KOMP), merely takes one step to achieve the sparse coefficients. A remarkable improvement (up to 2,750 times) in efficiency is reported for S-KOMP, with only a negligible loss of accuracy.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.publisherIEEE Computer Societyen_US
dc.publisher.placeLos Alamitos, CA, USAen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencename2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2011)en_US
dc.relation.ispartofconferencetitleProceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications DICTA 2011en_US
dc.relation.ispartofdatefrom2011-12-06en_US
dc.relation.ispartofdateto2011-12-08en_US
dc.relation.ispartoflocationNoosa, Queensland, Australiaen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchComputer Visionen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.subject.fieldofresearchcode080104en_US
dc.subject.fieldofresearchcode080109en_US
dc.titleFast Kernel Sparse Representationen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2011
gro.hasfulltextNo Full Text


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