A new LDA-KL combined method for feature extraction and its generalisation

Jian Yang, Hui Ye, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Linear discriminant analysis (LDA) is a well-known feature extraction technique. In this paper, we point out that LDA is not perfect because it only utilises the discriminatory information existing in the first-order statistical moments and ignores the information contained in the second-order statistical moments. We enhance LDA using the idea of a K-L expansion technique and develop a new LDA-KL combined method, which can make full use of both sections of discriminatory information. The proposed method is tested on the Concordia University CENPARMI handwritten numeral database. The experimental results indicate that the proposed LDA-KL method is more powerful than the existing techniques of LDA, K-L expansion and their combination: OLDA-PCA. What is more, the proposed method is further generalised to suit for feature extraction in the complex feature space and can be an effective tool for feature fusion.
Original languageEnglish
Pages (from-to)40-50
Number of pages11
JournalPattern Analysis and Applications
Issue number1
Publication statusPublished - 1 Jan 2004


  • Feature fusion
  • Handwritten numeral recognition
  • K-L expansion, feature extraction
  • Linear discriminant analysis (LDA)

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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