Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/70690
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dc.contributor.author | Shen, C. | - |
dc.contributor.author | Kim, J. | - |
dc.contributor.author | Wang, L. | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 11): pp.2601-2608 | - |
dc.identifier.isbn | 9781457703942 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/2440/70690 | - |
dc.description.abstract | Distance metric learning plays an important role in many vision problems. Previous work of quadratic Maha-lanobis metric learning usually needs to solve a semidef- inite programming (SDP) problem. A standard interior-point SDP solver has a complexity of O((D6.5)(with D the dimension of input data), and can only solve problems up to a few thousand variables. Since the number of vari- ables is D(D + 1)/2, this corresponds to a limit around D < 100. This high complexity hampers the application of metric learning to high-dimensional problems. In this work, we propose a very efficient approach to this metric learning problem. We formulate a Lagrange dual approach which is much simpler to optimize, and we can solve much larger Mahalanobismetric learning problems. Roughly, the proposed approach has a time complexity of O(t •D3) with t _ 20 _ 30 for most problems in our experiments. The proposed algorithm is scalable and easy to implement. Ex- periments on various datasets show its similar accuracy compared with state-of-the-art. We also demonstrate that this idea may also be able to be applied to other SDP problems such as maximum variance unfolding. | - |
dc.description.statementofresponsibility | Chunhua Shen, Junae Kim and Lei Wang | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE Conference on Computer Vision and Pattern Recognition | - |
dc.rights | Copyright status unknown | - |
dc.source.uri | http://dx.doi.org/10.1109/cvpr.2011.5995447 | - |
dc.title | A scalable dual approach to semidefinite metric learning | - |
dc.type | Conference paper | - |
dc.contributor.conference | IEEE Conference on Computer Vision and Pattern Recognition (24th : 2011 : Colorado Springs, CO, U.S.A.) | - |
dc.identifier.doi | 10.1109/CVPR.2011.5995447 | - |
dc.publisher.place | USA | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Shen, C. [0000-0002-8648-8718] | - |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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