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Supervised Feature Extraction Algorithm Based on Continuous Divergence Criterion

  • Shifei Ding
  • Zhongzhi Shi
  • Fengxiang Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

Feature extraction plays an important part in pattern recognition (PR), data mining, machine learning et al. In this paper, a novel supervised feature extraction algorithm based on continuous divergence criterion (CDC) is set up. Firstly, the concept of the CDC is given, and some properties of the CDC are studied, and proved that CDC here is a kind of distance measure, i.e. it satisfies three requests of distance axiomatization, which can be used to measure the difference degree of a two-class problem. Secondly, based on CDC, the basic principle of supervised feature extraction are studied, a new concept of accumulated information rate (AIR) is given, which can be used to measure the degree of feature compression for two-class, and a new supervised feature extraction algorithm is constructed. At last, the experimental results demonstrate that the algorithm here is valid and reliable.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shifei Ding
    • 1
    • 2
  • Zhongzhi Shi
    • 2
  • Fengxiang Jin
    • 3
  1. 1.College of Information Science and EngineeringShandong Agricultural UniversityTaianP.R. China
  2. 2.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R. China
  3. 3.College of Geo-Information Science and EngineeringShandong University of Science and TechnologyQingdaoP.R. China

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