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An Algorithm for Extraction of More Than One Optimal Linear Feature from Several Gaussian Pattern Classes.
RICE UNIV HOUSTON TEX DEPT OF ELECTRICAL ENGINEERING
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Two algorithms were developed at Rice University for optimal linear feature extraction based on the minimization of the risk probability of misclassification under the assumption that the class conditional probability density functions are Gaussian. In the present report, the second algorithm is described which is used when the dimension of the feature space is greater than one. Numerical results obtained from the application of the present algorithm to remotely sensed data from the Purdue C1 flight line are mentioned. Brief comparisons are made of these results with those obtained using a feature selection technique based on maximizing the Bhattacharyya distance. For the example considered, a significant improvement in classification is obtained by the present technique.
APPROVED FOR PUBLIC RELEASE