t-SNE works by converting the high-dimensional Euclidean distances between data points into conditional probabilities that represent similarities. It then minimizes the Kullback-Leibler divergence between these probabilities in the high-dimensional space and their counterparts in the low-dimensional space. This process involves two main steps:
Computing pairwise similarities between data points in the high-dimensional space Optimizing the positions of points in the low-dimensional space to best match these similarities
