Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/70690
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dc.contributor.authorShen, C.-
dc.contributor.authorKim, J.-
dc.contributor.authorWang, L.-
dc.date.issued2011-
dc.identifier.citationProceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 11): pp.2601-2608-
dc.identifier.isbn9781457703942-
dc.identifier.issn1063-6919-
dc.identifier.urihttp://hdl.handle.net/2440/70690-
dc.description.abstractDistance 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.statementofresponsibilityChunhua Shen, Junae Kim and Lei Wang-
dc.language.isoen-
dc.publisherIEEE-
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognition-
dc.rightsCopyright status unknown-
dc.source.urihttp://dx.doi.org/10.1109/cvpr.2011.5995447-
dc.titleA scalable dual approach to semidefinite metric learning-
dc.typeConference paper-
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (24th : 2011 : Colorado Springs, CO, U.S.A.)-
dc.identifier.doi10.1109/CVPR.2011.5995447-
dc.publisher.placeUSA-
pubs.publication-statusPublished-
dc.identifier.orcidShen, C. [0000-0002-8648-8718]-
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