MetricMap: An embedding technique for processing distance-based queries in metric spaces

Document Type

Article

Publication Date

10-1-2005

Abstract

In this paper, we present an embedding technique, called MetricMap, which is capable of estimating distances in a pseudometric space. Given a database of objects and a distance function for the objects, which is a pseudometric, 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 an embedding technique can be used as an approximate oracle to process a broad class of distance-based queries. It is also adaptable to data mining applications such as data clustering and classification. We present the theory underlying MetricMap and conduct experiments to compare MetricMap with other methods including MVP-tree and M-tree in processing the distance-based queries. Experimental results on both protein and RNA data show the good performance and the superiority of MetricMap over the other methods. © 2005 IEEE.

Identifier

26844498681 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics

External Full Text Location

https://doi.org/10.1109/TSMCB.2005.848489

ISSN

10834419

PubMed ID

16240772

First Page

973

Last Page

987

Issue

5

Volume

35

Grant

IIS-9988345

Fund Ref

National Science Foundation

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