Dimensionality Reduction

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Dimensionality reduction is a technique used to make large, high-dimensional datasets easier to work with. It combines or transforms many related features into a smaller set that still represents most of the original information to reduce storage needs and make patterns easier to see.

There are two main types of methods:

  • Linear techniques, like Principal Component Analysis (PCA), compress data by finding directions that capture the most variation.
  • Nonlinear methods, such as t-SNE and UMAP, are often used for visualizing data in two or three dimensions while keeping similar points close together.

Good dimensionality reduction keeps the balance between simplifying data and preserving accuracy.

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