Pattern recognition by sequential sweeping along preferred directions

Abstract

This paper deals with a novel method of pattern recognition, in which recognition is effected by sequential sweeping along preferred directions. The chosen set of measurement directions (x₁, x₂, ....x_n) are first ordered according to preference and then the axes are swept sequentially in that order. An algorithm is proposed which gives a code representation for the pattern clusters in an n-dimensional pattern space. Two models are proposed for the recognizer. It is shown that the method is capable of 100% recognition of learning patterns, under the assumption that no two patterns belonging to different categories occupy the same pattern point in the pattern space. A further advantage of the method is that out of an initially chosen set of measurements (characteristics) the recognizer makes use only of an important sub-set. Thus a sort of 'measurement selection' is inherent in this type of pattern recognizer.