Visualizing Data using GTSNE
Machine Learning
2021-08-04 v1 Human-Computer Interaction
Optimization and Control
Machine Learning
Abstract
We present a new method GTSNE to visualize high-dimensional data points in the two dimensional map. The technique is a variation of t-SNE that produces better visualizations by capturing both the local neighborhood structure and the macro structure in the data. This is particularly important for high-dimensional data that lie on continuous low-dimensional manifolds. We illustrate the performance of GTSNE on a wide variety of datasets and compare it the state of art methods, including t-SNE and UMAP. The visualizations produced by GTSNE are better than those produced by the other techniques on almost all of the datasets on the macro structure preservation.
Cite
@article{arxiv.2108.01301,
title = {Visualizing Data using GTSNE},
author = {Songting Shi},
journal= {arXiv preprint arXiv:2108.01301},
year = {2021}
}