mlsanityData to 14 Oct 2026

Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE

arXiv 2406.12953 ↗First posted 18 Jun 2024cs.GR cs.HC cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

This paper presents TRACE, a tool to analyze the quality of 2D embeddings generated through dimensionality reduction techniques. Dimensionality reduction methods often prioritize preserving either local neighborhoods or global distances, but insights from visual structures can be misleading if the objective has not been achieved uniformly. TRACE addresses this challenge by providing a scalable and extensible pipeline for computing both local and global quality measures. The interactive browser-based interface allows users to explore various embeddings while visually assessing the pointwise embedding quality. The interface also facilitates in-depth analysis by highlighting high-dimensional nearest neighbors for any group of points and displaying high-dimensional distances between points. TRACE enables analysts to make informed decisions regarding the most suitable dimensionality reduction method for their specific use case, by showing the degree and location where structure is preserved in the reduced space.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 9 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

3 of 9 references.

References · 3

  1. 2020Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualizationcited by 173
  2. 2020t-viSNE: Interactive Assessment and Interpretation of t-SNE Projectionscited by 43
  3. 2023ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddingscited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.