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Discriminative Representative Selection via Structure Sparsity
Wang, Baoxing; Yin, Qiyue; Wu, Shu; Wang, Liang
Conference NameInternational Conference on Pattern Recognition (ICPR)
Source PublicationIn Proceedings of the 22nd International Conference on Pattern Recognition (ICPR) 2014
Conference DateAugust 24-28
Conference PlaceStockholm
AbstractThis paper focuses on the problem of finding a few representatives for a given dataset, which have both representation and discrimination ability. To solve this problem, we propose a novel algorithm, called Structure Sparsity based Discriminative Representative Selection (SSDRS), to find a representative subset of data points. The selected representative subset keeps the representation ability based on sparse representation models assuming that each data point can be expressed as a linear combination of those representatives. Meanwhile, we employ the Fisher discrimination criterion to make the coefficient matrix possess small within-class scatter but big between-class scatter, which leads to the discriminant ability of representatives. Since such a selected subset is representative and discriminative, it can be used to properly describe the entire dataset and achieve a good classification performance simultaneously. Experimental results in terms of video summarization and image classification indicate that our proposed algorithm outperforms the state-ofthe-art methods.
KeywordRepresentative Selection Structure Sparsity
Document Type会议论文
Corresponding AuthorWu, Shu
Recommended Citation
GB/T 7714
Wang, Baoxing,Yin, Qiyue,Wu, Shu,et al. Discriminative Representative Selection via Structure Sparsity[C],2014.
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