# Multi-view Data Visualisation via Manifold Learning

arXiv: https://arxiv.org/abs/2101.06763 · HTML: https://mlsanity.com/paper/2101.06763/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 64 references that are in the map.
- First posted on arXiv 17 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Automatic Selection of t-SNE Perplexity](https://mlsanity.com/paper/1708.03229/) (2017)
- [Multi-View Intact Space Learning](https://mlsanity.com/paper/1904.02340/) (2019)
- [Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data](https://mlsanity.com/paper/1912.05449/) (2019)

## Built on by (most cited first, 7 of 7)

- [A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations](https://mlsanity.com/paper/2201.09656/) (2021)
- [ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE](https://mlsanity.com/paper/2205.11720/) (2022)
- [Unsupervised discovery of the shared and private geometry in multi-view data](https://mlsanity.com/paper/2408.12091/) (2024)
- [Consensus dimension reduction via multi-view learning](https://mlsanity.com/paper/2512.15802/) (2025)
- [Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering](https://mlsanity.com/paper/2604.23912/) (2026)
- [S-multi-SNE: Semi-Supervised Classification and Visualisation of Multi-View Data](https://mlsanity.com/paper/2111.03519/) (2021)
- [Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise](https://mlsanity.com/paper/2602.10530/) (2026)

## Abstract (arXiv)

Non-linear dimensionality reduction can be performed by textitmanifold learning approaches, such as Stochastic Neighbour Embedding (SNE), Locally Linear Embedding (LLE) and Isometric Feature Mapping (ISOMAP). These methods aim to produce two or three latent embeddings, primarily to visualise the data in intelligible representations. This manuscript proposes extensions of Student's t-distributed SNE (t-SNE), LLE and ISOMAP, for dimensionality reduction and visualisation of multi-view data. Multi-view data refers to multiple types of data generated from the same samples. The proposed multi-view approaches provide more comprehensible projections of the samples compared to the ones obtained by visualising each data-view separately. Commonly visualisation is used for identifying underlying patterns within the samples. By incorporating the obtained low-dimensional embeddings from the multi-view manifold approaches into the K-means clustering algorithm, it is shown that clusters of the samples are accurately identified. Through the analysis of real and synthetic data the proposed multi-SNE approach is found to have the best performance. We further illustrate the applicability of the multi-SNE approach for the analysis of multi-omics single-cell data, where the aim is to visualise and identify cell heterogeneity and cell types in biological tissues relevant to health and disease.