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Hyperparameters used in the algorithms for the Swiss Roll with increasing sparsity (see Figure 18)
| Algorithm | Hyperparameters | RES = 30 | RES = 15 | RES = 12 | RES = 10 |
|---|---|---|---|---|---|
| LDLE | η min | 3 | 3 | 3 | 3 |
| k tune | 7 | 2 | 2 | 2 | |
| N | 100 | 25 | 25 | 25 | |
| k lv | 7 | 4 | 4 | 4 | |
| LTSA | n_neighbors | 5 | 4 | 5 | 10 |
| UMAP | n_neighbors | 25 | 25 | 10 | 5 |
| min_dist | 0.01 | 0.01 | 0.5 | 0.5 | |
| t-SNE | perplexity | 10 | 5 | 5 | 5 |
| exaggeration | 4 | 2 | 4 | 2 |
