Low-dimensional Data Embedding via Robust Ranking
This work addresses the challenge of creating robust and efficient embeddings for data analysis, though it appears incremental as it builds on existing methods like t-SNE.
The authors tackled the problem of low-dimensional data embedding by introducing t-ETE, a method based on robust ranking over triplets, which produces high-quality embeddings with better noise tolerance and local scale preservation than t-STE and t-SNE, achieving significantly better results and faster computation on signature datasets.
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.