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Authors: Mateus Espadoto 1 ; 2 ; Nina S. T. Hirata 1 and Alexandru C. Telea 3

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

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

Abstract: Dimensionality reduction (DR) is used to explore high-dimensional data in many applications. Deep learning techniques such as autoencoders have been used to provide fast, simple to use, and high-quality DR. However, such methods yield worse visual cluster separation than popular methods such as t-SNE and UMAP. We propose a deep learning DR method called Self-Supervised Network Projection (SSNP) which does DR based on pseudo-labels obtained from clustering. We show that SSNP produces better cluster separation than autoencoders, has out-of-sample, inverse mapping, and clustering capabilities, and is very fast and easy to use.

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Paper citation in several formats:
Espadoto, M.; Hirata, N. and Telea, A. (2021). Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - IVAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 27-37. DOI: 10.5220/0010184800270037

@conference{ivapp21,
author={Mateus Espadoto. and Nina S. T. Hirata. and Alexandru C. Telea.},
title={Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - IVAPP},
year={2021},
pages={27-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010184800270037},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - IVAPP
TI - Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling
SN - 978-989-758-488-6
IS - 2184-4321
AU - Espadoto, M.
AU - Hirata, N.
AU - Telea, A.
PY - 2021
SP - 27
EP - 37
DO - 10.5220/0010184800270037
PB - SciTePress

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