ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE

TL;DR

ENS-t-SNE is a novel visualization algorithm that extends t-SNE to embed multiple neighborhood views in 3D, enabling simultaneous visualization of different cluster types within high-dimensional data.

Contribution

It introduces a generalized t-SNE method for 3D embedding that visualizes multiple cluster types simultaneously, improving interpretability over traditional 2D methods.

Findings

Enables visualization of different cluster types in a single 3D embedding.

Provides better tracking of clusters compared to multiple 2D plots.

Demonstrates effectiveness on various real-world datasets.

Abstract

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.