t-SNE operates 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 and low-dimensional spaces. This approach helps to preserve the local structure of the data while also revealing global patterns.
