Supervised learning using characteristic generalized Gaussian density and its application to Chinese materia medica identification

S. K. Choy, Chong Sze TONG

Research output: Chapter in book/report/conference proceedingChapterpeer-review

4 Citations (Scopus)

Abstract

This paper presents the estimation of the characteristic generalized Gaussian density (CGGD) given a set of known GGD distributions based on some optimization techniques, and its application to the Chinese Materia Medica identification. The CGGD parameters are estimated by minimizing the distance between the CGGD distribution and known GGD distributions. Our experimental results show that the proposed signature based on the CGGD together with the use of Kullback-Leibler distance outperforms the traditional wavelet-based energy signature. The recognition rate for the proposed method is higher than the energy signature by at least 10% to around 60% – 70%. Nevertheless, the extraction of CGGD estimators still retains comparable level of computational complexity. In general, our proposed method is very competitive compared with many other existing Chinese Materia Medica classification methods.

Original languageEnglish
Title of host publicationApplied and Numerical Harmonic Analysis
PublisherSpringer International Publishing
Pages443-452
Number of pages10
Edition9783764377779
DOIs
Publication statusPublished - 2007

Publication series

NameApplied and Numerical Harmonic Analysis
Number9783764377779
ISSN (Print)2296-5009
ISSN (Electronic)2296-5017

Scopus Subject Areas

  • Applied Mathematics

User-Defined Keywords

  • Generalized Gaussian density
  • Kullback-Leibler distance
  • Similarity measurement
  • Supervised learning
  • Wavelets

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