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Handwritten Digit Recognition Using Low Rank Approximation Based Competitive Neural Network

  • Yafeng Hu
  • Feng Zhu
  • Hairong Lv
  • Xianda Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

A novel approach for handwritten digit recognition is proposed in this paper, which combines the low rank approximation and the competitive neural network together. The images in each class are clustered into several subclasses by the competitive neural network, which is helpful for feature extraction. The low rank approximation is used for image feature extraction. Finally, the k-nearest neighbor classifier is applied to the classification. Experiment results on USPS dataset show the effectiveness of the proposed approach.

Keywords

Feature Extraction Training Image Feature Matrix Handwritten Digit Wine Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yafeng Hu
    • 1
  • Feng Zhu
    • 1
  • Hairong Lv
    • 1
  • Xianda Zhang
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina

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