t-distributed stochastic neighbor embedding (t-SNE) is a machine learning technique used for dimensionality reduction that helps visualize high-dimensional data in a low-dimensional space, typically two or three dimensions. By preserving local structures and the probabilities of similarities between data points, t-SNE effectively reveals clusters or patterns in complex datasets, making it valuable for both classical and quantum machine learning integration.
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