t-SNE

t-SNE is a technique used for dimensionality reduction, particularly well-suited for visualizing high-dimensional data.

t-SNE is commonly used in fields like bioinformatics, machine learning, and cognitive science to visualize clusters and patterns in high-dimensional datasets.

t-SNE works by converting the similarities between data points into joint probabilities and then minimizing the Kullback-Leibler divergence between these joint probabilities in the high-dimensional space and the low-dimensional space. This results in a map where similar objects are modeled by nearby points and dissimilar objects are modeled by distant points.

For example, consider a dataset of handwritten digits where each digit is represented by a high-dimensional vector of pixel values. t-SNE can be used to project this high-dimensional data into a two-dimensional space, where similar digits are placed closer together, making it easier to visualize the clusters of different digits.