S-multi-SNE: Semi-Supervised Classification and Visualisation of Multi-View Data

• URL: http://arxiv.org/abs/2111.03519v1
• Date: Fri, 5 Nov 2021 14:19:28 GMT
• Title: S-multi-SNE: Semi-Supervised Classification and Visualisation of Multi-View Data
• Authors: Theodoulos Rodosthenous and Vahid Shahrezaei and Marina Evangelou
• Abstract summary: Multi-view 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. Here, we extend multi-SNE to a semi-supervised approach, that classifies unlabelled samples by regarding the labelling information as an extra data-view.
• Score: 0.03222802562733786
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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