  t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of
multidimensional data has proven to be a popular approach, with successful
applications in a wide range of domains. Despite their usefulness, t-SNE
projections can be hard to interpret or even misleading, which hurts the
trustworthiness of the results. Understanding the details of t-SNE itself and
the reasons behind specific patterns in its output may be a daunting task,
especially for non-experts in dimensionality reduction. In this work, we
present t-viSNE, an interactive tool for the visual exploration of t-SNE
projections that enables analysts to inspect different aspects of their
accuracy and meaning, such as the effects of hyper-parameters, distance and
neighborhood preservation, densities and costs of specific neighborhoods, and
the correlations between dimensions and visual patterns. We propose a coherent,
accessible, and well-integrated collection of different views for the
visualization of t-SNE projections. The applicability and usability of t-viSNE
are demonstrated through hypothetical usage scenarios with real data sets.
Finally, we present the results of a user study where the tool's effectiveness
was evaluated. By bringing to light information that would normally be lost
after running t-SNE, we hope to support analysts in using t-SNE and making its
results better understandable.
