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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.
Index Terms:
Dimensionality reduction, feature extraction, nonlinear mapping, nonparametric, pattern recognition.
Citation:
T.W. Calvert, "Nonorthogonal Projections for Feature Extraction in Pattern Recognition," IEEE Transactions on Computers, vol. 19, no. 5, pp. 447-452, May 1970, doi:10.1109/T-C.1970.222943
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