A two-stage classification method for vector pattern matching problems

In this paper, we propose a new method to classify unknown objects into a large number of possible patterns (classes) and thereby solve vector-matching problems. The core of this method is a learning mechanism that reduces a huge amount of training samples into a highly condensed set of templates. When used in a testing process, these templates hold target patterns within the nearest K templates for almost all unknown objects, where K is a small number (2 or 3, for example). This learning mechanism also produces an extremely small set of confusing pairs, in opposition to all N(N-1)/2 possible pairs, where N is the total number of patterns. These pairs are further processed by a disambiguation procedure that improves the overall performance of the pattern classifier. This learning method thus suggests a two-stage online process for classifying unknown objects. The first stage reduces the number of possible candidates to a few candidates and the second stage identifies the target patterns out of these candidates.