Nonorthogonal projections for feature extraction in pattern recognition

It is known that R linearly separable classes of multi-dimensional pattern vectors can always be represented in a feature space of at most R dimensions. An approach is developed which can frequently be used to find a non-orthogonal transformation to project the patterns into a feature space of considerably lower dimensionality. Examples involving classification of handwritten and printed digits are used to illustrate the technique.