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A discriminant graph nonnegative matrix factorization approach to computer vision

  • Xiangguang Dai
  • Guo Chen
  • Chuandong Li
Original Article

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.

Keywords

Supervised learning Nonnegative matrix factorization Image recognition Dimensional reduction 

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing), College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.School of Electrical Engineering and TelecommunicationsThe University of New South WalesSydneyAustralia

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