Multidimensional Rotations in Feature Selection

An important aspect in mathematical pattern recognition is the usually noninvertible transformation from the pattern space to a reduced dimensionality feature space that allows a classification process to be implemented on a reasonable number of features. Such feature-selecting transformations range from simple coordinate stretching and shrinking to highly complex nonlinear extraction algorithms. A class of feature-selection transformations to which this note addresses itself is that given by multidimensional rotations. Unitary transformations of particular interest are the Karhunen-Loeve, Fourier, Hadamard or Walsh, and the Haar transforms. A character recognition experiment is selected for exemplary purposes and the use of features in the rotated spaces results in effective minimum distance classification.