A linear mapping technique for optimizing binary templates in noise-free pattern matching

Dimensionality reduction of noise-free binary patterns is achieved here by linear mapping from higher dimensional vector space to a lower one. A systematic methodology is thus obtained for finding better templates for a collection of noise free patterns, a priori knowledge of the probability distribution of which is not required. Here the memory requirement of the classifier is reduced and the template matching made faster under certain conditions.