Earticle

초록

영어

Semi-supervised constraint scores, which utilize both pairwise constraints and the local property of the unlabeled data to select features, achieve comparable performance to the supervised feature selection methods. The local property is characterized without considering the pairwise constraints and these two conditions are introduced independently. However, the pairwise constraints and the local property may contain conflicting information. In this paper, we utilize the conflicting information to improve the local property. Instead of characterizing the local property by all neighbors, samples which do not appear in the cannot-link constraints can be used. A performance indicator, called neighborhood-cannot-link (NC) coefficient, is proposed to measure the improvement of the local property. We use the improved local property and the pairwise constraints to perform semi-supervised constraint scores algorithm. Experiments on several real world data sets demonstrate the effectiveness of the methods.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Laplacian Score
  2.2. Constraint Scores
 3. NC Coefficient and MCS
  3.1. NC Coefficient
  3.2. Modified Constraint Scores
 4. Experimental Results
  4.1. Experimental Setting
  4.2. Yale Face Database
  4.3. UCI Database
  4.4. HRRP Data Set
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Jianqiao Wang [ School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Yuehua Li [ School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Kun Chen [ School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605