Visualizing Data using GTSNE
This addresses visualization challenges for high-dimensional data analysis, though it appears incremental as a variation of existing techniques.
The paper tackles the problem of visualizing high-dimensional data by introducing GTSNE, a variation of t-SNE that better captures both local and macro structures, resulting in improved visualizations compared to state-of-the-art methods like t-SNE and UMAP on most datasets.
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.