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Neural Processing Letters

, Volume 49, Issue 1, pp 203–226 | Cite as

Learning Robust Weighted Group Sparse Graph for Discriminant Visual Analysis

  • Tan GuoEmail author
  • Xiaoheng Tan
  • Lei Zhang
  • Qin Liu
  • Lu Deng
  • Chaochen Xie
Article

Abstract

Recently, sparse representation (SR) based graph has been successfully applied for dimensionality reduction (DR). However, the unsupervised characteristic of SR may cause instable representation results, which is undesired for graph construction. To alleviate the problem, a robust weighted group sparse representation (RWGSR) method is developed by minimizing the combination of l1-norm regularized representation fidelity and the weighted l2,1-norm regularized representation coefficients. RWGSR can find the robust and stable intrinsic intra-class and inter-class adjacent relations of samples. The intra-class and inter-class representations of RWGSR are then utilized to construct corresponding intra-class and inter-class graphs. With the graphs, a novel supervised DR algorithm named robust weighted group sparse graph based embedding (RWGSE) is proposed. Benefitting from RWGSR, RWGSE considers both intra-class and inter-class intrinsic structures of data, and seeks a low-dimensional subspace by simultaneously minimizing the intra-class scatter and maximizing the inter-class scatter. Extensive experiments on public benchmark face and object datasets show the effectiveness of the proposed method.

Keywords

Dimensionality reduction Graph embedding Group sparse Image classification 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers and the editors for their valuable suggestions. This work was supported by the National Natural Science Foundation of China (Nos. 61571069, 61771079), Chongqing University Postgraduates’ Innovation Project (No. CYB15030), and in part by the Fundamental Research Funds for the Central Universities (Nos. 106112017CDJQJ168817, 106112017CDJQJ168819).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Tan Guo
    • 1
    Email author
  • Xiaoheng Tan
    • 1
  • Lei Zhang
    • 1
  • Qin Liu
    • 1
  • Lu Deng
    • 1
  • Chaochen Xie
    • 1
  1. 1.College of Communication EngineeringChongqing UniversityChongqingChina

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