An improved LDA approach

Xiao Yuan Jing, Dapeng Zhang, Yuan Yan Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

58 Citations (Scopus)


Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness. The first weakness is that not all the discrimination vectors that are obtained are useful in pattern classification. Second, it remains computationally expensive to make the discrimination vectors completely satisfy statistical uncorrelation. The third weakness is that it is necessary to select the appropriate principal components. In this paper, we propose to improve discrimination technique in these three areas and to that end present an improved LDA (ILDA) approach which synthesizes these improvements. Experimental results on different image databases demonstrate that our improvements on LDA are efficient, and that ILDA outperforms other state-of-the-art linear discrimination methods.
Original languageEnglish
Pages (from-to)1942-1951
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number5
Publication statusPublished - 1 Oct 2004

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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