Fast<tex>$k$</tex>-NN Classification Using the Cluster-Space Approach

Abstract

A fast k-nearest neighbor algorithm is presented which combines k-NN with a cluster-space data representation. Implementation of the algorithm is easier, and classification time can be significantly reduced. Computer-generated data show the modified k-NN retains the advantage of nonparametric analysis but with significant reduction in computational load. Results from tests carried out with Hyperion data demonstrate that the simplification has little effect on classification performance, and yet efficiency is greatly improved.