t-Distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for dimensionality reduction developed by Laurens van der Maaten and Geoffrey Hinton. It is particularly well-suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot. The core of t-SNE lies in modeling the similarities between data points in high-dimensional space and then reconstructing these similarities in a low-dimensional space.
