t-distributed stochastic neighbor embedding (t-SNE) is a machine learning algorithm developed by Laurens van der Maaten and Geoffrey Hinton for dimensionality reduction. It is particularly well-suited for the visualization of high-dimensional datasets. By converting similarities between data points to joint probabilities and minimizing the Kullback-Leibler divergence between these joint probabilities in the low-dimensional space, t-SNE effectively reveals the structure within the data.
