A Nonlinear Feature Extraction Algorithm Using Distance Transrormation WARREN

Feature extraction has been recognized as a useful technique for pattern recognition. Feature extraction is accomplished by constructing a mapping from the measurement space to a feature space. Often, the mapping is chosen from an arbitrarily specified parametric family by optimizing the parameters with respect to a separability criterion. In the approach described here, a scalar distance function is produced using one-dimensional function approximation. The multivariate mapping, which produces the features, is determined in a straightforward manner from the distance function. Index Terms Distance functions, feature extraction, nonlinear mapping, pattern recognition, preprocessing.