Fast k-NN classification rule using metric on space-filling curves
Ewa Skubalska-Rafajłowicz, Adam Krzyżak · 1996
A fast nearest neighbor algorithm for pattern classification is proposed and tested on real data. The patterns (points in d-dimensional Euclidean space) are sorted along a space-filling curve. This way the multi-dimensional problem is compressed to the simplest case of the nearest neighbor search in one dimension. Instead of Euclidean distance a metric on space-filling curve is used. The method may be inferior or superior to the k-NN rule in multidimensional Euclidean space.