Symmetric Generative Methods and tSNE: A Short Survey

Rodolphe Priam

2018

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.

Paper Citation

in Harvard Style

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 (VISIGRAPP 2018) - Volume 2: IVAPP; ISBN 978-989-758-289-9, SciTePress, pages 356-363. DOI: 10.5220/0006684303560363

in Bibtex Style

@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 (VISIGRAPP 2018) - Volume 2: IVAPP},
year={2018},
pages={356-363},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006684303560363},
isbn={978-989-758-289-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 2: 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
PB - SciTePress