Semi-Supervised Classification and Visualization of Multi-View Data

• Activity Number: 288 - SLDS CSpeed 5
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Theodoulos Rodosthenous* and Vahid Shahrezaei and Marina Evangelou
• Companies: Imperial College London and Imperial College London and Imperial College London
• Keywords: Data visualisation; Dimensionality reduction; Semi-supervised classification; t-SNE; Multi-view data; Manifold learning

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.