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

arXiv:2205.11720v3 Announce Type: replace
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 …

abstract algorithm arxiv cs.ds cs.hc cs.lg data dataset embedding stochastic type types view