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Understanding How Dimension Reduction Tools Work: An Empirical Approach
  to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

8 December 2020
Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik
ArXivPDFHTML

Papers citing "Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization"

1 / 1 papers shown
Title
T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data
T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data
Hugo Thimonier
José Lucas De Melo Costa
Fabrice Popineau
Arpad Rimmel
Bich-Liên Doan
23
1
0
07 Oct 2024
1