Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107728
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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Eriksson, A. | - |
dc.contributor.author | Pham, T. | - |
dc.contributor.author | Chin, T. | - |
dc.contributor.author | Reid, I. | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, vol.07-12-June-2015, pp.3349-3357 | - |
dc.identifier.isbn | 9781467369640 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/2440/107728 | - |
dc.description.abstract | Sparsity, or cardinality, as a tool for feature selection is extremely common in a vast number of current computer vision applications. The k-support norm is a recently proposed norm with the proven property of providing the tightest convex bound on cardinality over the Euclidean norm unit ball. In this paper we present a re-derivation of this norm, with the hope of shedding further light on this particular surrogate function. In addition, we also present a connection between the rank operator, the nuclear norm and the k-support norm. Finally, based on the results established in this re-derivation, we propose a novel algorithm with significantly improved computational efficiency, empirically validated on a number of different problems, using both synthetic and real world data. | - |
dc.description.statementofresponsibility | Anders Eriksson, Trung Thanh Pham, Tat-Jun Chin, Ian Reid | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE Conference on Computer Vision and Pattern Recognition | - |
dc.rights | © 2015 IEEE | - |
dc.source.uri | http://dx.doi.org/10.1109/cvpr.2015.7298956 | - |
dc.subject | Optimization, computer science, computer vision, computational modeling, convex functions, convergence, electrical engineering | - |
dc.title | The k-support norm and convex envelopes of cardinality and rank | - |
dc.type | Conference paper | - |
dc.contributor.conference | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) (7 Jun 2015 - 12 Jun 2015 : Boston, MA) | - |
dc.identifier.doi | 10.1109/CVPR.2015.7298956 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DE130101775 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/CE140100016 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/FL130100102 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Reid, I. [0000-0001-7790-6423] | - |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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RA_hdl_107728.pdf Restricted Access | Restricted Access | 784.42 kB | Adobe PDF | View/Open |
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