  Widely used pipelines for the analysis of high-dimensional data utilize
two-dimensional visualizations. These are created, e.g., via t-distributed
stochastic neighbor embedding (t-SNE). When it comes to large data sets,
applying these visualization techniques creates suboptimal embeddings, as the
hyperparameters are not suitable for large data. Cranking up these parameters
usually does not work as the computations become too expensive for practical
workflows. In this paper, we argue that a sampling-based embedding approach can
circumvent these problems. We show that hyperparameters must be chosen
carefully, depending on the sampling rate and the intended final embedding.
Further, we show how this approach speeds up the computation and increases the
quality of the embeddings.
