  We introduce "TriMap"; a dimensionality reduction technique based on triplet
constraints, which preserves the global structure of the data better than the
other commonly used methods such as t-SNE, LargeVis, and UMAP. To quantify the
global accuracy of the embedding, we introduce a score that roughly reflects
the relative placement of the clusters rather than the individual points. We
empirically show the excellent performance of TriMap on a large variety of
datasets in terms of the quality of the embedding as well as the runtime. On
our performance benchmarks, TriMap easily scales to millions of points without
depleting the memory and clearly outperforms t-SNE, LargeVis, and UMAP in terms
of runtime.
