Abstract :
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.
Keywords :
Dimensionality reduction, feature extraction, nonlinear mapping, nonparametric, pattern recognition.; Biotechnology; Character recognition; Diseases; Extraterrestrial measurements; Feature extraction; Medical diagnosis; Multidimensional systems; Pattern recognition; Probability density function; Speech recognition; Dimensionality reduction, feature extraction, nonlinear mapping, nonparametric, pattern recognition.;
