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@unpublished{Rodosthenous:2021,
author = {Rodosthenous, T and Shahrezaei, V and Evangelou, M},
publisher = {arXiv},
title = {S-multi-SNE: Semi-supervised classification and visualisation of multi-view data},
url = {https://www.imperial.ac.uk/people/theodoulos.rodosthenous16},
year = {2021}
}

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TY  - UNPB
AB  - An increasing number of multi-view data are being published by studies in several fields. This type of data corresponds to multiple data-views, each representing a different aspect of the same set of samples. We have recently proposed multi-SNE, an extension of t-SNE, that produces a single visualisation of multi-view data. The multi-SNE approach provides low-dimensional embeddings of the samples, produced by being updated iteratively through the different data-views. Here, we further extend multi-SNE to a semi-supervised approach, that classifies unlabelled samples by regarding the labelling information as an extra data-view. We look deeper into the performance, limitations and strengths of multi-SNE and its extension, S-multi-SNE, by applying the two methods on various multi-view datasets with different challenges. We show that by including the labelling information, the projection of the samples improves drastically and it is accompanied by a strong classification performance.
AU  - Rodosthenous,T
AU  - Shahrezaei,V
AU  - Evangelou,M
PB  - arXiv
PY  - 2021///
TI  - S-multi-SNE: Semi-supervised classification and visualisation of multi-view data
UR  - https://www.imperial.ac.uk/people/theodoulos.rodosthenous16
UR  - https://arxiv.org/abs/2111.03519
ER  -

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