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.

Abstract: We propose an algorithm for visualizing a dataset by embedding it in 3-dimensional Euclidean space based on various given distances between the same pairs of datapoints. Its aim is to find an Embedding which preserves Neighborhoods Simultaneously for all given distances by generalizing the t-Stochastic Neighborhood Embedding approach (ENS-t-SNE). We illustrate the utility of ENS-t-SNE by demonstrating its use in three applications. First, to visualize different notions of clusters and groups within the same high-dimensional dataset with one 3-dimensional embedding, as opposed to providing different embeddings of the same data and trying to match the corresponding points. Second, to illustrate the effects of different hyper-parameters of the classical t-SNE.