Work

A Fast kNN Algorithm Using Multiple Space-Filling Curves

Year: 2022

Type: article

Abstract: nearest neighbours (kNN) algorithm. A well-known approach for accelerating the kNN algorithm is to utilise dimensionality reduction methods based on the use of space-filling curves. In this paper, we take this approach further and propose an algorithm that employs multiple space-filling curves and is faster (with comparable quality) compared with the kNN algorithm, which uses kd-trees to determine the nearest neighbours. A specific method for constructing multiple Peano curves is outlined, and statements are given about the preservation of object proximity information in the course of dimensionality reduction. An experimental comparison with known kNN implementations using kd-trees was performed using test and real-life data.

Source: Entropy

DOI: 10.3390/e24060767

Authors: Konstantin Barkalov, Anton Shtanyuk, Alexander Sysoyev

Institution: N. I. Lobachevsky State University of Nizhny Novgorod

Language: English

Topic: Data Management and Algorithms

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,885

Funder: Ministry of Science and Higher Education of the Russian Federation