dimensionality reduction:

What are Some Common Techniques Used for Dimensionality Reduction?

Common techniques include:
Principal Component Analysis (PCA): Reduces data by identifying principal components that explain the most variance.
t-Distributed Stochastic Neighbor Embedding (t-SNE): Useful for visualizing high-dimensional data by mapping it to two or three dimensions.
Linear Discriminant Analysis (LDA): Focuses on maximizing the separability among known categories.

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