  We describe a new method called t-ETE for finding a low-dimensional embedding
of a set of objects in Euclidean space. We formulate the embedding problem as a
joint ranking problem over a set of triplets, where each triplet captures the
relative similarities between three objects in the set. By exploiting recent
advances in robust ranking, t-ETE produces high-quality embeddings even in the
presence of a significant amount of noise and better preserves local scale than
known methods, such as t-STE and t-SNE. In particular, our method produces
significantly better results than t-SNE on signature datasets while also being
faster to compute.
