The Beginner's Guide To Dimensionality Reduction by University of Washington
Dimensionality reduction is a powerful technique used by data scientists to look for hidden structure, producing visualizations that reveal patterns and clusters in data. Even though the data is displayed in only two or three dimensions, structures present in higher dimensions are maintained, at least roughly. This interactive guide will teach you how to think about these embeddings, and provide a comparison of some of the most popular dimensionality reduction algorithms used today.
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CreditsMatthew Conlen and Fred Hohman