SNE vs. t-SNE vs. UMAP: An Evolutionary Guide

Dimension reduction techniques are crucial in data science for visualization and pre-processing in machine learning. Three popular dimensionality reduction techniques are SNE (Stochastic Neighbor Embedding), t-SNE (t-distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection). These neighbor graph algorithms follow a similar process, starting with computing high-dimensional probabilities p, then low-dimensional probabilities q. The cost function C(p,q) is calculated by comparing the differences between probabilities, which is then minimized to obtain human-interpretable information from the embedding space.