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ABSTRACT
<p><b>Abstract</b>—Most fast <tmath>k{\hbox{-}}{\rm{nearest}}</tmath> neighbor (<tmath>k{\hbox{-}}{\rm{NN}}</tmath>) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate <tmath>k{\hbox{-}}{\rm{NN}}</tmath> classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.</p>
INDEX TERMS
Nearest neighbor classification, nonmetrics, metrics, cluster tree.
CITATION
Bin Zhang, Sargur N. Srihari, "Fast k-Nearest Neighbor Classification Using Cluster-Based Trees", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 525-528, April 2004, doi:10.1109/TPAMI.2004.1265868
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