Linear discriminant feature extraction using weighted classification confusion information

Hung Shin Lee, Berlin Chen

研究成果: 雜誌貢獻文章

11 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)2254-2257
頁數4
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版狀態已發佈 - 2008

ASJC Scopus subject areas

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

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