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논문검색

Research on Feature Extraction based on Deep Learning

원문정보

Wu Pin, Yan Hongjie, Shang Weilie, Zhu Yonghua, Gao Honghao

보안공학연구지원센터(IJHIT) International Journal of Hybrid Information Technology Vol.8 No.11 2015.11 pp.113-120
피인용수 : 0건 (자료제공 : 네이버학술정보)

초록

영어

With the development of deep learning, it has achieved impressive results in feature extraction field. This paper drives research in feature extraction based on deep learning. First, this paper gives a brief introduction on the world's research status on deep learning and principle of Restricted Boltzmann machine (RBM). Then this paper conducts reducing experiment based on RBM for handwritten digits. According to the analysis based on the results of the experiments, this paper tries to get a proper dimension which handwritten digits reduced to achieve better performance. Finally, this paper finds that it reach the goal when handwritten digits is reduced to half dimensional raw digits. This is an important foundation of deep learning layering and offers help to researchers in feature extraction based on deep learning.

목차

Abstract
 1. Introduction
 2. World Research Statuses
 3. RBM
 4. Experiment
  4.1. Experiment Environment and Conditions
  4.2. Experiment Instruction
  4.3. Experiment Process
  4.4. Experiment Result
 5. Conclusion
 References

키워드

저자정보

  • Wu Pin School of Computer Engineering and Science, Shanghai University, 200444, Shanghai, China Institute of Materials Genome, Shanghai University, Shanghai, China Computing Center, Shanghai University, 200444, Shanghai, China
  • Yan Hongjie School of Computer Engineering and Science, Shanghai University, 200444, Shanghai, China Institute of Materials Genome, Shanghai University, Shanghai, China Computing Center, Shanghai University, 200444, Shanghai, China
  • Shang Weilie School of Computer Engineering and Science, Shanghai University, 200444, Shanghai, China Institute of Materials Genome, Shanghai University, Shanghai, China Computing Center, Shanghai University, 200444, Shanghai, China
  • Zhu Yonghua School of Computer Engineering and Science, Shanghai University, 200444, Shanghai, China Institute of Materials Genome, Shanghai University, Shanghai, China Computing Center, Shanghai University, 200444, Shanghai, China
  • Gao Honghao School of Computer Engineering and Science, Shanghai University, 200444, Shanghai, China Institute of Materials Genome, Shanghai University, Shanghai, China Computing Center, Shanghai University, 200444, Shanghai, China

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