Enhance explainability of manifold learning

The explainability of manifold learning is rarely investigated though there is an urgent need from both AI theory and practice. In this study, we propose a novel degree of locality preservation (DLP) approach to study the Our study provides well-founded explanations of the manifold learning methods in terms of the DLPs. The order of their DLPs follows t-SNE UMAP LLE