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Fast nearest-neighbor search algorithms based on approximation-elimination search

โœ Scribed by V. Ramasubramanian; Kuldip K. Paliwal

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
272 KB
Volume
33
Category
Article
ISSN
0031-3203

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โœฆ Synopsis

In this paper, we provide an overview of fast nearest-neighbor search algorithms based on an &approxima-tion}elimination' framework under a class of elimination rules, namely, partial distance elimination, hypercube elimination and absolute-error-inequality elimination derived from approximations of Euclidean distance. Previous algorithms based on these elimination rules are reviewed in the context of approximation}elimination search. The main emphasis in this paper is a comparative study of these elimination constraints with reference to their approximation}elimination e$ciency set within di!erent approximation schemes.

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Classifying an unknown input is a fundamental problem in Pattern Recognition. One standard method is "nding its nearest neighbors in a reference set. It would be very time consuming if computed feature by feature for all templates in the reference set; this namK ve method is O(nd) where n is the num