Linear discriminant feature extraction using weighted classification confusion information

Hung Shin Lee*, Berlin Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Linear discriminant analysis (LDA) can be viewed as a two-stage procedure geometrically. The first stage conducts an orthogonal and whitening transformation of the variables. The second stage involves a principal component analysis (PCA) on the transformed class means, which is intended to maximize the class separability along the principal axes. In this paper, we demonstrate that the second stage does not necessarily guarantee better classification accuracy. Furthermore, we propose a generalization of LDA, weighted LDA (WLDA), by integrating the empirical classification confusion information between each class pair, such that the separability and the classification error rate can be taken into consideration simultaneously. WLDA can be efficiently solved by a lightweight eigen-decomposition and easily combined with other modifications to the LDA criterion. The experiment results show that WLDA can yield a relative character error reduction of 4.6% over LDA on the Mandarin LVCSR task.

Original languageEnglish
Pages (from-to)2254-2257
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2008


  • Confusion information
  • Feature extraction
  • Linear discriminant analysis
  • Speech recognition

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems


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