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Authors: Mateus Espadoto 1 ; Nina Hirata 1 ; Alexandre Falcão 2 and Alexandru Telea 3

Affiliations: 1 Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil ; 2 Institute of Computing, University of Campinas, Campinas, Brazil ; 3 Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands

ISBN: 978-989-758-402-2

ISSN: 2184-4321

Keyword(s): Dimensionality Reduction, Machine Learning, Neural Networks, Multidimensional Projections.

Abstract: Dimensionality reduction methods are often used to explore multidimensional data in data science and information visualization. Techniques of the SNE-class, such as t-SNE, have become the standard for data exploration due to their good visual cluster separation, but are computationally expensive and don’t have out-of-sample capability by default. Recently, a neural network-based technique was proposed, which adds out-of-sample capability to t-SNE with good results, but with the disavantage of introducing some diffusion of the points in the result. In this paper we evaluate many neural network-tuning strategies to improve the results of this technique. We show that a careful selection of network architecture, loss function and data augmentation strategy can improve results.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Espadoto, M.; Hirata, N.; Falcão, A. and Telea, A. (2020). Improving Neural Network-based Multidimensional Projections. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, ISBN 978-989-758-402-2 ISSN 2184-4321, pages 29-41. DOI: 10.5220/0008877200290041

@conference{ivapp20,
author={Mateus Espadoto. and Nina Hirata. and Alexandre Falcão. and Alexandru Telea.},
title={Improving Neural Network-based Multidimensional Projections},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,},
year={2020},
pages={29-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008877200290041},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,
TI - Improving Neural Network-based Multidimensional Projections
SN - 978-989-758-402-2
IS - 2184-4321
AU - Espadoto, M.
AU - Hirata, N.
AU - Falcão, A.
AU - Telea, A.
PY - 2020
SP - 29
EP - 41
DO - 10.5220/0008877200290041

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