An approximate oracle for distance in metric spaces

Document Type

Conference Proceeding

Publication Date

1-1-1998

Abstract

In this paper we present a new data structure for estimating distances in a pseudo-metric space. Given are a database of objects and a distance function for the objects, which is a pseudo-metric. We map the objects to vectors in a pseudo-Euclidean space with a reasonably low dimension while preserving the distance between two objects approximately. Such a data structure can be used as an approximate oracle to process a broad class of pattern-matching based queries. Experimental results on both synthetic and real data show the good performance of the oracle in distance estimation.

Identifier

84877330430 (Scopus)

ISBN

[3540647392, 9783540647393]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/bfb0030784

e-ISSN

16113349

ISSN

03029743

First Page

104

Last Page

117

Volume

1448 LNCS

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