English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Faithful visualizations of data residing on manifolds must take theunderlying geometry into account when producing a flat planar view of the data.In this paper, we extend the classic stochastic neighbor embedding (SNE)algorithm to data on general Riemannian manifolds. We replace standard Gaussianassumptions with Riemannian diffusion counterparts and propose an efficientapproximation that only requires access to calculations of Riemannian distancesand volumes. We demonstrate that the approach also allows for mapping data fromone manifold to another, e.g. from a high-dimensional sphere to alow-dimensional one.

Visualizing Riemannian data with Rie-SNE