Low-dimensional Data Embedding via Robust Ranking
Artificial Intelligence
2017-05-18 v2 Machine Learning
Machine Learning
Abstract
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.
Cite
@article{arxiv.1611.09957,
title = {Low-dimensional Data Embedding via Robust Ranking},
author = {Ehsan Amid and Nikos Vlassis and Manfred K. Warmuth},
journal= {arXiv preprint arXiv:1611.09957},
year = {2017}
}