ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE
This is an incremental improvement for researchers and practitioners in data visualization, offering a more integrated way to explore cluster structures in complex datasets.
The paper tackles the problem of visualizing high-dimensional datasets by proposing ENS-t-SNE, an algorithm that generalizes t-SNE to create a 3D embedding where different viewpoints reveal various cluster types, enabling easier tracking compared to multiple 2D embeddings.
When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data. We describe ENS-t-SNE: an algorithm for Embedding Neighborhoods Simultaneously that generalizes the t-Stochastic Neighborhood Embedding approach. By using different viewpoints in ENS-t-SNE's 3D embedding, one can visualize different types of clusters within the same high-dimensional dataset. This enables the viewer to see and keep track of the different types of clusters, which is harder to do when providing multiple 2D embeddings, where corresponding points cannot be easily identified. We illustrate the utility of ENS-t-SNE with real-world applications and provide an extensive quantitative evaluation with datasets of different types and sizes.