Low-dimensional Data Embedding via Robust Ranking

2016/11/30 by Ehsan Amid, Nikos Vlassis, Amid, Ehsan +3
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1611.09957

openalex publication_date 2016/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• Landmark Ordinal Embedding
• ReliefE: Feature Ranking in High-dimensional Spaces via Manifold\n Embeddings
• Factoring out prior knowledge from low-dimensional embeddings
• A more globally accurate dimensionality reduction method using triplets
• Dimension-Accuracy Tradeoffs in Contrastive Embeddings for Triplets, Terminals & Top-k Nearest Neighbors