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Genetic generalized discriminant analysis and its applications

  • Lijun Yan
  • , Linlin Tang*
  • , Shu Chuan Chu
  • , Xiaorui Zhu
  • , Jun Bao Li
  • , Xiaochuan Guo
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen
  • Flinders University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a novel Genetic Generalized Discriminant Analysis (GGDA) is proposed. GGDA is a generalized version of Exponential Discriminant Analysis (EDA). EDA algorithm is equivalent to map the samples to a new space and then perform LDA. However, is this space is optimal for classification? The proposed GGDA uses Genetic Algorithm to search for an more discriminant diffusing map and then perform LDA in the new space. The Experimental results confirm the efficiency of the proposed algorithm.

Original languageEnglish
Title of host publicationModern Advances in Applied Intelligence - 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Proceedings
EditorsMoonis Ali, Jeng-Shyang Pan, Mong-Fong Horng, Shyi-Ming Chen
PublisherSpringer Verlag
Pages246-255
Number of pages10
ISBN (Electronic)9783319074542
DOIs
StatePublished - 2014
Event27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014 - Kaohsiung, Taiwan, Province of China
Duration: 3 Jun 20146 Jun 2014

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume8481
ISSN (Print)0302-9743

Conference

Conference27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014
Country/TerritoryTaiwan, Province of China
CityKaohsiung
Period3/06/146/06/14

Keywords

  • Exponential discriminant analysis
  • Feature extraction
  • Genetic algorithm

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