Fast k-Nearest Neighbor Classification Using Cluster-Based Trees

Authors:
Bin Zhang;Sargur N. Srihari
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2004

Citing 13
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Abstract

Abstract--Most fast k{\hbox{-}}{\rm{nearest}} neighbor (k{\hbox{-}}{\rm{NN}}) 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 k{\hbox{-}}{\rm{NN}} 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.