Effective proximity retrieval by ordering permutations.

Full Abstract

We introduce a new probabilistic proximity search algorithm for range and K-nearest neighbor (K-NN) searching in both coordinate and metric spaces. Although there exist solutions for these problems, they boil down to a linear scan when the space is intrinsically high-dimensional, as is the case in many pattern recognition tasks. This, for example, renders the K-NN approach to classification rather slow in large databases. Our novel idea is to predict closeness between elements according to how they order their distances towards a distinguished set of anchor objects. Each element in the space sorts the anchor objects from closest to farthest to it, and the similarity between orders turns out to be an excellent predictor of the closeness between the corresponding elements. We present extensive experiments comparing our method against state-of-the-art exact and approximate techniques, both in synthetic and real, metric and non-metric databases, measuring both CPU time and distance computations. The experiments demonstrate that our technique almost always improves upon the performance of alternative techniques, in some cases by a wide margin.

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Author information

Author/s: Chavez, Edgar (E); Figueroa, Karina (K); Navarro, Gonzalo (G);

Affiliation: Facultad de Ciencias Fisico Matematicas, Universidad Michoacana, Ciudad Universitaria, Michoacan, Mexico. elchavez(-atsign-)fismat.umich.mx

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2008-Sep; vol 30 (issue 9) : pp 1647-58

Dates: Created 2008/07/11; Completed 2008/09/23;

PMID: 18617721, status: MEDLINE (last retrieval date: 11/6/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Image Interpretation, Computer-Assisted - methods
Information Storage and Retrieval - methods
Models, Statistical
Pattern Recognition, Automated - methods
Subtraction Technique

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