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

TL;DR

S-multi-SNE is a semi-supervised extension of multi-SNE that enhances visualization and classification of multi-view data by incorporating label information as an additional view, significantly improving sample projection and classification accuracy.

Contribution

This paper introduces S-multi-SNE, a novel semi-supervised method that integrates label information into multi-view data visualization and classification.

Findings

Inclusion of label information drastically improves sample projection.

S-multi-SNE achieves strong classification performance.

The method effectively handles various multi-view datasets with different challenges.

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.