Improved AURA k-nearest neighbour approach

Michael Weeks*, Vicky Hodge, Simon O'Keefe, Jim Austin, Ken Lees

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The k-Nearest Neighbour (kNN) approach is a widely-used technique for pattern classification. Ranked distance measurements to a known sample set determine the classification of unknown samples. Though effective, kNN, like most classification methods does not scale well with increased sample size. This is due to their being a relationship between the unknown query and every other sample in the data space. In order to make this operation scalable, we apply AURA to the kNN problem. AURA is a highly-scalable associative-memory based binary neural-network intended for high-speed approximate search and match operations on large unstructured datasets. Previous work has seen AURA methods applied to this problem as a scalable, but approximate kNN classifier. This paper continues this work by using AURA in conjunction with kernel-based input vectors, in order to create a fast scalable kNN classifier, whilst improving recall accuracy to levels similar to standard kNN implementations.

Original languageEnglish
Pages (from-to)663-670
Number of pages8
JournalLecture Notes in Computer Science
Volume2687
Publication statusPublished - 1 Dec 2003

Cite this