T-SNE

T-SNE (t-Distributed Stochastic Neighbor Embedding) is a machine learning algorithm used for data visualization. It is a non-linear dimensionality reduction technique that is particularly well-suited for embedding high-dimensional data into a low-dimensional space of two or three dimensions, which can then be visualized in a scatter plot. T-SNE works by minimizing the divergence between two probability distributions: a distribution that measures pairwise similarities between the high-dimensional data points and a distribution that measures pairwise similarities between the corresponding low-dimensional points. By minimizing this divergence, T-SNE is able to preserve the local structure of the data, meaning that nearby points in the high-dimensional space remain nearby in the low-dimensional space. T-SNE is commonly used in fields such as computer vision, natural language processing, and bioinformatics.
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