Earticle

초록

영어

According to the error approximation problem the sparse preserving projections (SPP) reconstruct the original sample. This paper proposes the algorithms based on orthogonal sparse preserving projections of kernel. In order to get sparse representation coefficients that contain more identification information by kernel method, it mapped samples to high-dimensional feature space to. Then, reconstructing sparse coefficient of kernel sparse representation increase the similar non neighbor sample weight, and reduce heterogeneous neighbor sample weight. Finally, the whole orthogonal constraint transformation improve the ability of sparse retain sample. The algorithm experiments were carried out on the YALE_B and ORL face database, and the recognition rate reached 96.3%, and the results verify the effectiveness and robustness of the algorithm.

목차

Abstract
 1. Introduction
 2. Sparse Preserving Projections
 3. Orthogonal Sparse Preserving Projections of Kernel
 4. Experimental Analysis
  4.1. Experiments on the ORL Face Database
  4.2. Experimental on YALE_B Face Database
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Kezheng Lin [ Department of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China ]
• Di Wu [ Department of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China ]
• Youhu Rong [ Department of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China ]
• Liangwei Zhuang [ Department of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605