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Exploiting label consistency in structured sparse representation for classification

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Abstract

Sparse representation with adaptive dictionaries has emerged as a promising tool in computer vision and pattern analysis. While standard sparsity promoted by \(\ell _0\) or \(\ell _1\) regularization has been widely used, recent approaches seek for kinds of structured sparsity to improve the discriminability of sparse codes. For classification, label consistency is one useful concept regarding structured sparsity, which relates class labels to dictionary atoms for generating discriminative sparsity patterns. Motivated by the limitations of existing label-consistent regularization methods, in this paper, we investigate the exploitation of label consistency and propose an effective sparse coding approach. The proposed approach enforces the sparse approximation of a label consistency matrix by sparse code during dictionary learning, which encourages the supports of sparse codes to be consistent for intra-class signals and distinct for inter-class signals. Thus, the learned dictionary can induce discriminative sparsity patterns when used in sparse coding. Moreover, the proposed method is computationally efficient, as the label consistency regularization developed in our method brings very little additional computational cost in solving the related sparse coding problem. The effectiveness of the proposed method is demonstrated with several recognition tasks, and the experimental results show that our method is very competitive with some state-of-the-art approaches.

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Notes

  1. Such a kind of sparsity is often referred to as structured sparsity.

  2. This is often true if the corresponding signal \({\varvec{y}}_1\) and \({\varvec{y}}_2\) are from different subclasses.

  3. For rigorous proof, the sequence \(\{{\varvec{C}}^{(k)}\}_k\) is required to be bounded, which can be guaranteed by simple projection of \({\varvec{C}}^{(k)}\) at each iteration.

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Acknowledgements

Yuhui Quan would like to thank the support by National Natural Science Foundation of China (61602184), Science and Technology Planning Project of Guangdong Province (2017A030313376), Science and Technology Program of Guangzhou (201707010147) and Educational Reform Project of South China University of Technology (j2jwY9160960). Yong Xu would like to thank the support by National Natural Science Foundation of China (U16114616167224161602184 and 61528204) and Cultivation Project of Major Basic Research of NSF-Guangdong Province (2016A030308013).

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  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Yan Huang, Yuhui Quan, Tao Liu & Yong Xu

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  1. Yan Huang
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  2. Yuhui Quan
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  3. Tao Liu
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Correspondence to Yuhui Quan.

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Huang, Y., Quan, Y., Liu, T. et al. Exploiting label consistency in structured sparse representation for classification. Neural Comput & Applic 31, 6509–6520 (2019). https://doi.org/10.1007/s00521-018-3479-0

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