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Symmetric generative methods and tSNE: a short survey

Symmetric generative methods and tSNE: a short survey
Symmetric generative methods and tSNE: a short survey

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.

Data Visualization, Generative Model, Latent Variables, Survey, TSNE
356-363
Scitepress
Priam, Rodolphe
f9b703b2-b814-4e19-83e6-b1ca0ec5c0e4
Telea, Alexandru
Kerren, Andreas
Braz, Jose
Priam, Rodolphe
f9b703b2-b814-4e19-83e6-b1ca0ec5c0e4
Telea, Alexandru
Kerren, Andreas
Braz, Jose

Priam, Rodolphe (2018) Symmetric generative methods and tSNE: a short survey. Telea, Alexandru, Kerren, Andreas and Braz, Jose (eds.) In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP. vol. 3, Scitepress. pp. 356-363 .

Record type: Conference or Workshop Item (Paper)

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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More information

Published date: 2018
Venue - Dates: 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018, Portugal, 2018-01-27 - 2018-01-29
Keywords: Data Visualization, Generative Model, Latent Variables, Survey, TSNE

Identifiers

Local EPrints ID: 421551
URI: http://eprints.soton.ac.uk/id/eprint/421551
PURE UUID: ed7188bc-d649-4849-9746-fe900bf4b532

Catalogue record

Date deposited: 15 Jun 2018 16:30
Last modified: 07 Oct 2020 00:20

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Contributors

Author: Rodolphe Priam
Editor: Alexandru Telea
Editor: Andreas Kerren
Editor: Jose Braz

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