On the Extraction of Pattern Features from Continuous Measurements

Abstract

A suboptimum method of extracting features, by linear operations, from continuous data belonging to M pattern classes is presented. The set of features selected minimizes bounds on the probability of error obtained from the Bhattacharyya distance and the Hajek divergence. The random processes associated with the pattern classes are assumed to be Gaussian with different means and covariance functions. For M=2, in the two special cases in which, respectively, the means and the covariance functions are the same, both the above distance measures yield the same answer. The results obtained represent an extension of the existing results for two pattern classes with the same means and different covariance functions.