A Declustering Criterion for Feature Extraction in Pattern Recognition

A feature extraction technique based on a new criterion for "declustering" is presented. Declustering occurs when sample vectors from one pattern class form a densely packed point constellation, or cluster, in feature space while vectors from another class do not form a cluster but instead array themselves as outliers. Features chosen to optimize the declustering criterion enhance class separation and are robust over a wide range of measurement statistics.