Abstract
Many investigators reporting on feature extraction algorithms expect a decrease in recognition accuracy as an inevitable consequence of information loss. A feature extraction procedure is introduced which through empirical study indicates an improvement in recognition rate beyond that of a maximum likelihood classifier while permitting computational economy as a result of dimensionality reduction. Average divergence is shown to have increased after the application of the feature extraction procedure.
Date of this Version
1975
COinS