ADAPTIVE REORDERING OF OBSERVATION SPACE TO IMPROVE PATTERN RECOGNITION

Abstract

The problem of observation space reordering is presented as a novel approach to pattern recognition based on non-parametric, combinatorial statistical tests. It consists in linearly ordering the elements of a discrete multi-dimensional observation space along a curve such that elements belonging to different similarity classes are as close to each other as possible, the similarity classes are mutually separated, and the length of the curve is kept to minimum. The problem is NP-difficult and it is shown how its approximate solution can be reached by a series of transformations improving the initial lexicographic linear order of a discrete observation space. Recommendations are formulated for linear order improvement leading to a pattern recognition algorithm based on serial statistical test.