Decision Boundary Feature Extraction for Nonparametric Classification

Chulhee Lee, David A. Landgrebe

Research output: Contribution to journalArticle

33 Citations (Scopus)


Feature extraction has long been an important topic in pattern recognition. Although many authors have studied feature extraction for parametric classifiers, relatively few feature extraction algorithms are available for nonparametric classifiers. A new feature extraction algorithm based on decision boundaries for nonparametric classifiers is proposed. It is noted that feature extraction for pattern recognition is equivalent to retaining “discriminantly informative features” and a discriminantly informative feature is related to the decision boundary. Since nonparametric classifiers do not define decision boundaries in analytic form, the decision boundary and normal vectors must be estimated numerically. A procedure to extract discriminantly informative features based on a decision boundary for nonparametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.

Original languageEnglish
Pages (from-to)433-444
Number of pages12
JournalIEEE Transactions on Systems, Man and Cybernetics
Issue number2
Publication statusPublished - 1993 Jan 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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