A Dimensionality Reduction Method for Data Visualization using Particle Swarm Optimization

Panagiotis C. Petrantonakis, Ioannis Kompatsiaris

2020

Abstract

Dimensionality reduction involves mapping of a set of high dimensional input points on a low dimensional space. Mappings in low dimensional space are expected to preserve the pairwise distances of the high dimensional inputs. In this work we present a dimensionality reduction method, called Dimensionality Reduction based on Particle Swarm Optimization (PSO-DR), where the conversion of each input to the low dimensional output does not depend on the rest of the inputs but, instead, it is based on a set of reference points (beacons). The presented approach results in a simple, fast, versatile dimensionality reduction approach with good quality of visualization and straightforward out-of-sample extension.

Paper Citation

in Harvard Style

Petrantonakis P. and Kompatsiaris I. (2020). A Dimensionality Reduction Method for Data Visualization using Particle Swarm Optimization. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA; ISBN 978-989-758-475-6, SciTePress, pages 131-138. DOI: 10.5220/0010020601310138

in Bibtex Style

@conference{ecta20,
author={Panagiotis C. Petrantonakis and Ioannis Kompatsiaris},
title={A Dimensionality Reduction Method for Data Visualization using Particle Swarm Optimization},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA},
year={2020},
pages={131-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010020601310138},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA
TI - A Dimensionality Reduction Method for Data Visualization using Particle Swarm Optimization
SN - 978-989-758-475-6
AU - Petrantonakis P.
AU - Kompatsiaris I.
PY - 2020
SP - 131
EP - 138
DO - 10.5220/0010020601310138
PB - SciTePress