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A discriminant graph nonnegative matrix factorization approach to computer vision

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Abstract

This paper proposes a novel dimensional reduction method, called discriminant graph nonnegative matrix factorization (DGNMF), for image representation. Inspired by manifold learning and linear discrimination analysis, DGNMF provides a compact representation which can respect the original data space. In addition, In addition, the within-class distance of each class in the representation is very small. Based on these characteristics, our proposed method can be viewed as a supervised learning method, which outperforms some existing dimensional reduction methods, including PCA, LPP, LDA, NMF and GNMF. Experiments on image recognition have shown that our approach can provide a better representation than some classic methods.

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Authors and Affiliations

  1. National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing), College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Xiangguang Dai & Chuandong Li

  2. School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW, 2052, Australia

    Xiangguang Dai & Guo Chen

Authors
  1. Xiangguang Dai
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  2. Guo Chen
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  3. Chuandong Li
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Corresponding authors

Correspondence to Guo Chen or Chuandong Li.

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Dai, X., Chen, G. & Li, C. A discriminant graph nonnegative matrix factorization approach to computer vision. Neural Comput & Applic 31, 7879–7889 (2019). https://doi.org/10.1007/s00521-018-3608-9

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  • DOI: https://doi.org/10.1007/s00521-018-3608-9

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