UMAP[1] and t-SNE[2] are two very similar and popular ways to map a set of points in a high dimensional space to a low-dimensional (usually 2D or 3D) space by a

UMAP[1] and t-SNE[2] are two very similar and popular ways to map a set of points in a high dimensional space to a low-dimensional (usually 2D or 3D) space by assuming all the points are close to some smooth curved surface (a manifold[3]). These are unsupervised non-linear dimensional reduction techniques; if you're familar with linear dimensional reduction techniques like PCA[4] you can think of UMAP and t-SNE as curved generalizations of that. t-SNE is widely known to be hard to interpret and unstable with respect to arbitrary hyper-parameters[5] and I would assume UMAP suffers similar problems. It's also extremely difficult to define "similarity" between documents and authors. Techniques like LSA[6] and word embeddings[7] exist but are still an open area of research. Based on similar concerns as yours, I doubt any of these issues were seriously addressed when this visualization was developed. [1]: https://arxiv.org/abs/1802.03426 https://arxiv.org/abs/1802.03426 [2]: https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding https://en.wikipedia.org/wiki/T-distributed_stochastic_neigh... [3]: https://en.wikipedia.org/wiki/Manifold https://en.wikipedia.org/wiki/Manifold [4]: https://en.wikipedia.org/wiki/Principal_component_analysis https://en.wikipedia.org/wiki/Principal_component_analysis [5]: https://stats.stackexchange.com/questions/263539/clustering-on-the-output-of-t-sne https://stats.stackexchange.com/questions/263539/clustering-... [6]: https://en.wikipedia.org/wiki/Latent_semantic_analysis https://en.wikipedia.org/wiki/Latent_semantic_analysis [7]: https://en.wikipedia.org/wiki/Word_embedding https://en.wikipedia.org/wiki/Word_embedding