dc.contributor.convenor | Andrew Bradley | |
dc.contributor.author | Li, H | |
dc.contributor.author | Gao, Y | |
dc.contributor.author | Sun, J | |
dc.contributor.editor | IEEE | |
dc.date.accessioned | 2017-05-03T15:01:12Z | |
dc.date.available | 2017-05-03T15:01:12Z | |
dc.date.issued | 2011 | |
dc.date.modified | 2012-02-20T05:51:30Z | |
dc.identifier.isbn | 9780769545882 | |
dc.identifier.refuri | http://itee.uq.edu.au/~dicta2011/ | |
dc.identifier.doi | 10.1109/DICTA.2011.20 | |
dc.identifier.uri | http://hdl.handle.net/10072/42997 | |
dc.description.abstract | Two 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. | |
dc.description.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.language | English | |
dc.publisher | IEEE Computer Society | |
dc.publisher.place | Los Alamitos, CA, USA | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofconferencename | 2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2011) | |
dc.relation.ispartofconferencetitle | Proceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011 | |
dc.relation.ispartofdatefrom | 2011-12-06 | |
dc.relation.ispartofdateto | 2011-12-08 | |
dc.relation.ispartoflocation | Noosa, Queensland, Australia | |
dc.relation.ispartofpagefrom | 72 | |
dc.relation.ispartofpageto | 77 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Computer vision | |
dc.subject.fieldofresearchcode | 460304 | |
dc.title | Fast Kernel Sparse Representation | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dc.type.code | E - Conference Publications | |
gro.faculty | Griffith Sciences, Griffith School of Engineering | |
gro.date.issued | 2011 | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Gao, Yongsheng | |