Clustering on UMIST Faces Cropped
0.1183ARIGTSA-PCA
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| GTSA-PCADimensionality Reduction Method=GTSA-PCA, Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.1183 | 0.1706 | 0.3922 | |
| Kernel PCA (RBF)Dimensionality Reduction Method=Kernel PCA (RBF), Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.1088 | 0.1767 | 0.3785 | |
| Kernel PCA (RBF)Feature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | 0.0485 | 0.207 | 0.169 | |
| Regular PCADimensionality Reduction Method=Regular PCA, Projection Dimension=2D, Clustering Algorithm=Agglomerative Clustering, Linkage=Ward2026.04 | 0.0377 | 0.1201 | 0.2712 | |
| GTSA-PCAFeature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | 0.0317 | 0.2069 | 0.1734 | |
| Regular PCAFeature Space=Two-dimensional, Clustering Algorithm=HDBSCAN2026.04 | 0.0097 | 0.1721 | 0.0552 |