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Author: Rodolphe Priam

Affiliation: University of Southampton, United Kingdom

ISBN: 978-989-758-289-9

Keyword(s): Data Visualization, Generative Model, Latent Variables, tSNE, Survey.

Related Ontology Subjects/Areas/Topics: Abstract Data Visualization ; Computer Vision, Visualization and Computer Graphics ; General Data Visualization ; High-Dimensional Data and Dimensionality Reduction ; Information and Scientific Visualization ; Visual Data Analysis and Knowledge Discovery

Abstract: In data visualization, a family of methods is dedicated to the symmetric numerical matrices which contain the distances or similarities between high-dimensional data vectors. The method t-Distributed Stochastic Neighbor Embedding and its variants lead to competitive nonlinear embeddings which are able to reveal the natural classes. For comparisons, it is surveyed the recent probabilistic and model-based alternative methods from the literature (LargeVis, Glove, Latent Space Position Model, probabilistic Correspondence Analysis, Stochastic Block Model) for nonlinear embedding via low dimensional positions.

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Paper citation in several formats:
Priam, R. (2018). Symmetric Generative Methods and tSNE: A Short Survey.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, ISBN 978-989-758-289-9, pages 356-363. DOI: 10.5220/0006684303560363

@conference{ivapp18,
author={Rodolphe Priam.},
title={Symmetric Generative Methods and tSNE: A Short Survey},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,},
year={2018},
pages={356-363},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006684303560363},
isbn={978-989-758-289-9},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,
TI - Symmetric Generative Methods and tSNE: A Short Survey
SN - 978-989-758-289-9
AU - Priam, R.
PY - 2018
SP - 356
EP - 363
DO - 10.5220/0006684303560363

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