In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These involve the transformation of high-dimensional into reduced versions, typically in 2D, with aim preserving significant properties from original data. Many algorithms exist, nonlinear approaches such as t-SNE (t-Distributed Stochastic Neighbor Embedding) UMAP ...