On the Extraction of Pattern Features from Continuous Measurements
- 1 April 1970
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems Science and Cybernetics
- Vol. 6 (2) , 110-115
- https://doi.org/10.1109/TSSC.1970.300284
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.Keywords
This publication has 4 references indexed in Scilit:
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- A class of upper bounds on probability of error for multihypotheses pattern recognition (Corresp.)IEEE Transactions on Information Theory, 1969
- On the extraction of pattern features from continuous measurementsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1969
- On the best finite set of linear observables for discriminating two Gaussian signalsIEEE Transactions on Information Theory, 1967