Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107728
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Type: | Conference paper |
Title: | The k-support norm and convex envelopes of cardinality and rank |
Author: | Eriksson, A. Pham, T. Chin, T. Reid, I. |
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 |
Publisher: | IEEE |
Issue Date: | 2015 |
Series/Report no.: | IEEE Conference on Computer Vision and Pattern Recognition |
ISBN: | 9781467369640 |
ISSN: | 1063-6919 |
Conference Name: | 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015) (7 Jun 2015 - 12 Jun 2015 : Boston, MA) |
Statement of Responsibility: | Anders Eriksson, Trung Thanh Pham, Tat-Jun Chin, Ian Reid |
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. |
Keywords: | Optimization, computer science, computer vision, computational modeling, convex functions, convergence, electrical engineering |
Rights: | © 2015 IEEE |
DOI: | 10.1109/CVPR.2015.7298956 |
Grant ID: | http://purl.org/au-research/grants/arc/DE130101775 http://purl.org/au-research/grants/arc/CE140100016 http://purl.org/au-research/grants/arc/FL130100102 |
Published version: | http://dx.doi.org/10.1109/cvpr.2015.7298956 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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