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Nonorthogonal Projections for Feature Extraction in Pattern Recognition

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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.

Published in:

Computers, IEEE Transactions on  (Volume:C-19 ,  Issue: 5 )

Date of Publication:

May 1970

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