Feature extraction using the Bhattacharyya distance

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)


The Bhattacharyya distance provides a valuable information in determining the effectiveness of a feature set and has been used as separability measure for feature selection. Recently, it is shown that it is feasible to predict the classification error accurately using the Bhattacharyya distance. The new formula makes it possible to estimate classification error between two classes within 1-2% margin. In this paper, we propose a new feature extraction method utilizing the result. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we estimate the classification error using the error estimation formula. Then we move the feature vector slightly in the direction so that the estimated classification error is decreased most rapidly. This can be done by taking gradient. Experiments show that the proposed method compare favorably with the conventional methods.

Original languageEnglish
Pages (from-to)2147-2150
Number of pages4
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 1997
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA
Duration: 1997 Oct 121997 Oct 15

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture


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