t-SNE is a manifold learning technique that learns low-dimensional embeddings for high-dimensional data. It is most often used for visualization purposes because it exploits the local relationships between data points and can hence capture non-linear structures in the data. Unlike other dimension reduction techniques like PCA, a learned t-SNE model can't be applied to new data. The t-SNE algorithm can be roughly summarized as two steps:
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