A visual PCA explainer walks through centering, eigenvectors, and variance capture
mdancho84 · x · 2026-07-28
A short visual explainer breaks down PCA as a dimensionality-reduction method, showing the main steps from centering and normalization to covariance estimation, eigenvector computation, sorting components, and projecting data.
The image also illustrates the geometric intuition: the data is rotated onto principal axes, with the first components capturing most of the variance and the remaining components contributing progressively less, as shown by the explained-variance table.
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