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Effective proximity retrieval by ordering permutations.

Edgar Chavez¹, Karina Figueroa, Gonzalo Navarro

¹Facultad de Ciencias Fisico Matematicas, Universidad Michoacana, Ciudad Universitaria, Michoacan, Mexico. elchavez@fismat.umich.mx

IEEE Transactions on Pattern Analysis and Machine Intelligence

|July 12, 2008

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We developed a new algorithm for faster proximity searches in high-dimensional data. This method predicts element closeness by analyzing their distance orderings to anchor objects, significantly improving performance in pattern recognition tasks.

Area of Science:

Computer Science
Machine Learning
Data Mining

Background:

High-dimensional data presents challenges for traditional search algorithms like K-nearest neighbor (K-NN).
Existing solutions often degrade to slow linear scans in intrinsically high-dimensional spaces, impacting pattern recognition and large database performance.

Purpose of the Study:

To introduce a novel probabilistic proximity search algorithm for efficient range and K-nearest neighbor (K-NN) searching.
To address the performance limitations of existing methods in high-dimensional coordinate and metric spaces.

Main Methods:

The algorithm predicts element proximity based on the ordering of distances to a set of anchor objects.
Each data element sorts anchor objects by distance, and similarity in these orderings indicates closeness between elements.

Related Experiment Videos

Main Results:

Extensive experiments show the new method outperforms state-of-the-art exact and approximate techniques.
Performance improvements were observed across synthetic and real-world metric and non-metric databases.
The algorithm demonstrated significant gains in both CPU time and distance computations.

Conclusions:

The proposed probabilistic proximity search algorithm offers a substantial performance improvement over existing methods.
This technique provides an efficient solution for K-NN classification and other pattern recognition tasks in large, high-dimensional datasets.