Decision Boundary Feature Extraction for Nonparametric Classification

Chulhee Lee, David A. Landgrebe

Research output: Contribution to journalArticlepeer-review

38 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

Bibliographical note

Funding Information:
Manuscript received October 17, 1991; revised April 20, 1992 and September 5, 1992. This work was supported in part by NASA under Grant NAGW-925. The authors are with the School of Electrical Engineering, Purdue University, West Lafayette, IN 47907. IEEE Log Number 9206215.

All Science Journal Classification (ASJC) codes

  • Engineering(all)


Dive into the research topics of 'Decision Boundary Feature Extraction for Nonparametric Classification'. Together they form a unique fingerprint.

Cite this