Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2010.5539934
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dc.titleSparse representation using nonnegative curds and whey
dc.contributor.authorLiu, Y.
dc.contributor.authorWu, F.
dc.contributor.authorZhang, Z.
dc.contributor.authorZhuang, Y.
dc.contributor.authorYan, S.
dc.date.accessioned2014-06-19T03:28:18Z
dc.date.available2014-06-19T03:28:18Z
dc.date.issued2010
dc.identifier.citationLiu, Y.,Wu, F.,Zhang, Z.,Zhuang, Y.,Yan, S. (2010). Sparse representation using nonnegative curds and whey. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 3578-3585. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/CVPR.2010.5539934" target="_blank">https://doi.org/10.1109/CVPR.2010.5539934</a>
dc.identifier.isbn9781424469840
dc.identifier.issn10636919
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71829
dc.description.abstractIt has been of great interest to find sparse and/or nonnegative representations in computer vision literature. In this paper we propose a novel method to such a purpose and refer to it as nonnegative curds and whey (NNCW). The NNCW procedure consists of two stages. In the first stage we consider a set of sparse and nonnegative representations of a test image, each of which is a linear combination of the images within a certain class, by solving a set of regression-type nonnegative matrix factorization problems. In the second stage we incorporate these representations into a new sparse and nonnegative representation by using the group nonnegative garrote. This procedure is particularly appropriate for discriminant analysis owing to its supervised and nonnegativity nature in sparsity pursuing. Experiments on several benchmark face databases and Caltech 101 image dataset demonstrate the efficiency and effectiveness of our nonnegative curds and whey method. ©2010 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CVPR.2010.5539934
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CVPR.2010.5539934
dc.description.sourcetitleProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
dc.description.page3578-3585
dc.description.codenPIVRE
dc.identifier.isiutNOT_IN_WOS
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