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Graph steered discriminative projections based on collaborative representation for Image recognition

  • Li LiuEmail author
  • Bin Zhang
  • Huaxiang Zhang
  • Na Zhang
Article
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Abstract

Dimensionality reduction techniques are commonly used for image recognition. We propose a graph steered dimensionality reduction method called Discriminative Projections based on Collaborative Representation (DPCR) by transforming the dimensionality reduction task into a graph embedding framework. DPCR utilizes the collaborative representation to construct within-class and between-class graphs. To improve the discriminative performance of dimensionality reduction, DPCR introduces the label information into graph building. The novel method not only avoids the difficulty of finding proper neighborhood but also inherits the merits of manifold learning methods and the robustness of collaborative representation techniques. Experiments on benchmark datasets demonstrate its effectiveness.

Keywords

Collaborative representation Graph embedding Dimensionality reduction Image recognition 

Notes

Acknowledgements

The work is supported by the National Natural Science Foundation of China (No.61702310 and 61772322), the Key Research and Development Foundation of Shandong Province (No. 2016GGX101009).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina

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