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.
Bibliographical noteFunding 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.
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