Geometry and algebra of linear maps, vectors, and matrices
Principal component analysis finds the directions in which your data varies most, then lets you describe each point with just a few of those directions instead of all the original features. It is the standard tool for dimensionality reduction, and under the hood it is SVD applied to your data.
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▶ PCA via SVD