Manifold Learning Embedding on Swiss roll (non-uniform (Beta(1, 4)) sampling)
0.995Trustworthinesst-SNE
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| t-SNE2026.07 | 0.995 | 0.988 | 0.887 | 0.862 | |
| UMAP2026.07 | 0.992 | 0.967 | 0.751 | 0.733 | |
| Shortest Path2026.07 | 0.991 | 0.972 | 0.899 | 0.882 | |
| MDS2026.07 | 0.986 | 0.974 | 0.88 | 0.865 | |
| EntroPath2026.07 | 0.981 | 0.985 | 0.875 | 0.855 | |
| Isomap2026.07 | 0.976 | 0.994 | 0.918 | 0.899 | |
| PCA2026.07 | 0.968 | 0.993 | 0.876 | 0.862 | |
| Diffusion Maps2026.07 | 0.957 | 0.976 | 0.725 | 0.726 | |
| Laplacian Eigenmaps2026.07 | 0.949 | 0.976 | 0.716 | 0.721 | |
| DTNE2026.07 | 0.949 | 0.987 | 0.805 | 0.789 | |
| PHATE2026.07 | 0.945 | 0.984 | 0.741 | 0.737 | |
| HeatGeo2026.07 | 0.943 | 0.983 | 0.714 | 0.707 |