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It is known that R linearly separable classes of multidimensional 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 nonorthogonal 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.
Dimensionality reduction, feature extraction, nonlinear mapping, nonparametric, pattern recognition.

T. Calvert, "Nonorthogonal Projections for Feature Extraction in Pattern Recognition," in IEEE Transactions on Computers, vol. 19, no. , pp. 447-452, 1970.
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