Regularized discriminant entropy analysis

Haitao Zhao, Wai Keung Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


In this paper, we propose the regularized discriminant entropy (RDE) which considers both class information and scatter information on original data. Based on the results of maximizing the RDE, we develop a supervised feature extraction algorithm called regularized discriminant entropy analysis (RDEA). RDEA is quite simple and requires no approximation in theoretical derivation. The experiments with several publicly available data sets show the feasibility and effectiveness of the proposed algorithm with encouraging results.
Original languageEnglish
Pages (from-to)806-819
Number of pages14
JournalPattern Recognition
Issue number2
Publication statusPublished - 1 Feb 2014


  • Discriminant entropy analysis
  • Entropy-based learning
  • Regularized discriminant entropy

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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