On feature extraction for limited class problem

Abstract

The availability of the canonical discriminant analysis to a limited class problem is restricted because the number of extracted features can not be or exceed the number of classes. In order to remove the restriction, a new feature extraction technique FKL is proposed and is tested by handwritten numeral recognition experiment. While the canonical discriminant analysis maximizes the variance ratio (F-ratio), and the principal component analysis (K-L expansion) minimizes the mean square error of dimension reduction, the FKL optimizes both the F-ratio and the mean square error simultaneously. The result of experiment shows that the FKL provides the richest features in discriminating power for the limited class problem when compared with other techniques including the canonical discriminant analysis, the principal component analysis, and the orthonormal discriminant vector method (ODV).