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 proceedingConference contributionpeer-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 publicationWavelet Analysis and Applications
EditorsTao Qian, Mang I. Vai, Yuesheng Xu
PublisherBirkhäuser Basel - Springer
Pages443-452
Number of pages10
Edition1st
ISBN (Electronic)9783764377786
ISBN (Print)9783764377779
DOIs
Publication statusPublished - 2007
EventWavelet Analysis and Applications 2005 - University of Macau, Macao
Duration: 29 Nov 20052 Dec 2005

Publication series

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

Conference

ConferenceWavelet Analysis and Applications 2005
Country/TerritoryMacao
Period29/11/052/12/05

Scopus Subject Areas

  • Applied Mathematics

User-Defined Keywords

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

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